2023 15 3 0 137

Artificial intelligence (AI): The next stage of evolution? 2 2 The emergence of technology has long been a defining characteristic of human civilization, and in our current era, artificial intelligence (AI) stands as one of the most advanced innovations. Through the integration of AI into machines, the aim has been to unlock unprecedented levels of convenience. However, we now find ourselves at a crucial juncture where a significant question arises: Do humans continue to hold dominion, or have AI-equipped machines taken the reins? With its profound ability to reshape human capabilities, it is not surprising to propose that AI may represent the next stage of evolution. As we delve deeper into the potential of AI, it becomes imperative to ponder whether the emergence of AI as a form of evolved human beings is inevitable, and if so, what implications it may hold for the future of humanity. Taken together, it is essential for society to ensure the development and deployment of AI in a manner that prioritizes the safety and well-being of humanity while also giving careful consideration to ethical and legal concerns. 1 3 2023/06/25 1402/4/4 2023/08/17 1402/5/26 Davood Bashash Davood Bashash Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran david_5980@yahoo.com Mohammad Faranoush Mohammad Faranoush Pediatric Growth and Development Research Center, Iran University of Medical Sciences, Tehran, Iran faranoush47@gmail.com Technology Artificial intelligence (AI) Machine learning Evolution Baidoo-Anu D, Owusu Ansah L. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484. 2023.##Mazzone M, Elgammal A, editors. Art, creativity, and the potential of artificial intelligence. Arts; 2019: MDPI.##Chen L, Chen P, Lin Z. Artificial intelligence in education: A review. Ieee Access. 2020;8:75264-78.##Long E, Wan P, Chen Q, Lu Z, Choi J. From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence. Cell Genomics. 2023;3(6).##Tai MC-T. The impact of artificial intelligence on human society and bioethics. Tzu-Chi Medical Journal. 2020;32(4):339.##Aloisi A, De Stefano V. Your boss is an algorithm: artificial intelligence, platform work and labour: Bloomsbury Publishing; 2022.##Sandberg A. An overview of models of technological singularity. The Transhumanist Reader: Classical and Contemporary Essays on the Science, Technology, and Philosophy of the Human Future. 2013:376-94.##Fang J, Su H, Xiao Y. Will Artificial Intelligence Surpass Human Intelligence? Available at SSRN 3173876. 2018.##Khanam S, Tanweer S, Khalid S, Rosaci D. Artificial intelligence surpassing human intelligence: factual or hoax. The Computer Journal. 2019;64(12):1832-9.##Bostrom N. Ethical issues in advanced artificial intelligence. Machine Ethics and Robot Ethics. 2020:69-75.##Brendel AB, Mirbabaie M, Lembcke T-B, Hofeditz L. Ethical Management of Artificial Intelligence. Sustainability. 2021;13(4):1974.##Cath C. Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2018;376(2133):20180080.##UNESCO. Artificial Intelligence: examples of ethical dilemmas 2023 [Available from: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics/cases#:~:text=But%20there%20are%20many%20ethical,and%20privacy%20of%20court%20users.##Banja JD, Hollstein RD, Bruno MA. When artificial intelligence models surpass physician performance: medical malpractice liability in an era of advanced artificial intelligence. Journal of the American College of Radiology. 2022;19(7):816-20.##Jarrahi MH, Lutz C, Newlands G. Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation. Big Data & Society. 2022;9(2):20539517221142824.##Vrontis D, Christofi M, Pereira V, Tarba S, Makrides A, Trichina E. Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The International Journal of Human Resource Management. 2022;33(6):1237-66.##Jaiswal A, Arun CJ, Varma A. Rebooting employees: Upskilling for artificial intelligence in multinational corporations. The International Journal of Human Resource Management. 2022;33(6):1179-208.## ##

Beyond human capacity: How artificial intelligence (AI) is enhancing cancer diagnosis and treatment 2 2 In recent years, artificial intelligence (AI) has revolutionized several aspects of human life. The availability of high-dimensionality datasets with progression in high-performance computing, and innovative deep learning architectures which are the subdomains of AI, have led to promising functions of AI in the medical contexts, particularly in oncology. Regarding the capacity of AI models in recognition and learning patterns as well as associations, these systems can be utilized in various aspects of cancer research including cancer diagnosis and treatment. To be precise, AI models are able to analyze medical images such as stained histopathology slides and radiology images and consequently pave the way for cancer diagnosis, grading, classification, tumor characterization, and prognosis prediction. Moreover, AI algorithms can assess a myriad of medical data to recognize patterns and make predictions about patient treatment outcomes, enabling more personalized treatment plans. Accordingly, AIassisted cancer treatment strategies have been shown to notably improve the quality of cancer treatment with chemotherapy, immunotherapy, and even radiotherapy while reducing the treatment toxicities.  4 12 2023/06/252023/06/24 1402/4/3 2023/08/172023/08/13 1402/5/22 Mehrnaz sadat Ravari Mehrnaz sadat Ravari Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran mehranzarvari1991@gmail.com Majid Momeny Majid Momeny The Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA Majid.momeny@gmail.com Artificial intelligence (AI) Deep learning Prediction Cancer Diagnosis Treatment McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine. 2006;27(4):12-.##LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.##Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31.##Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021;11(4):900-15.##Chen Z, Li X, Yang M, Zhang H, Xu XS. Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering. J Pathol Clin Res. 2023;9(1):3-17.##Papadakis GZ, Karantanas AH, Tsiknakis M, Tsatsakis A, Spandidos DA, Marias K. Deep learning opens new horizons in personalized medicine. Biomed Rep. 2019;10(4):215-7.##Zhang S, Bamakan SMH, Qu Q, Li S. Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective. IEEE Rev Biomed Eng. 2019;12:194-208.##Zeng J, Cruz-Pico CX, Saridogan T, Shufean MA, Kahle M, Yang D, et al. Natural Language Processing-Assisted Literature Retrieval and Analysis for Combination Therapy in Cancer. JCO Clin Cancer Inform. 2022;6:e2100109.##Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. 2017;73:14-29.##Vaishya R, Misra A, Vaish A. ChatGPT: Is this version good for healthcare and research? Diabetes Metab Syndr. 2023;17(4):102744.##Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603-7.##Menden MP, Iorio F, Garnett M, McDermott U, Benes CH, Ballester PJ, et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS One. 2013;8(4):e61318.##Liu M, Shen X, Pan W. Deep reinforcement learning for personalized treatment recommendation. Stat Med. 2022;41(20):4034-56.##Yang CY, Shiranthika C, Wang CY, Chen KW, Sumathipala S. Reinforcement learning strategies in cancer chemotherapy treatments: A review. Comput Methods Programs Biomed. 2023;229:107280.##Chiu YC, Zheng S, Wang LJ, Iskra BS, Rao MK, Houghton PJ, et al. Predicting and characterizing a cancer dependency map of tumors with deep learning. Sci Adv. 2021;7(34).##Wainberg M, Merico D, Delong A, Frey BJ. Deep learning in biomedicine. Nat Biotechnol. 2018;36(9):829-38.##Meyer P, Noblet V, Mazzara C, Lallement A. Survey on deep learning for radiotherapy. Comput Biol Med. 2018;98:126-46.##Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021;41(11):1100-15.##Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-57.##Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017;60(6):84-90.##Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.##Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018;359(6378):926-30.##Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep. 2017;7:46479.##https://doi.org/10.1038/srep46878##Kang G, Liu K, Hou B, Zhang N. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One. 2017;12(11):e0188290.##Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, et al. 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas. Cancer Res. 2018;78(24):6881-9.##Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging. 2016;35(5):1207-16.##Yuan Z, Xu T, Cai J, Zhao Y, Cao W, Fichera A, et al. Development and Validation of an Image-based Deep Learning Algorithm for Detection of Synchronous Peritoneal Carcinomatosis in Colorectal Cancer. Ann Surg. 2022;275(4):e645-e51.##Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep. 2017;7(1):15415.##Neisius U, El-Rewaidy H, Nakamori S, Rodriguez J, Manning WJ, Nezafat R. Radiomic Analysis of Myocardial Native T(1) Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy. JACC Cardiovasc Imaging. 2019;12(10):1946-54.##van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights into Imaging. 2020;11(1):91.##Wentzensen N, Lahrmann B, Clarke MA, Kinney W, Tokugawa D, Poitras N, et al. Accuracy and Efficiency of Deep-Learning-Based Automation of Dual Stain Cytology in Cervical Cancer Screening. J Natl Cancer Inst. 2021;113(1):72-9.##Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68(10):1813-9.##Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. Jama. 2017;318(22):2199-210.##Singhal N, Soni S, Bonthu S, Chattopadhyay N, Samanta P, Joshi U, et al. A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies. Sci Rep. 2022;12(1):3383.##Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature. 2021;594(7861):106-10.##Montalban‐Bravo G, Garcia‐Manero G. Myelodysplastic syndromes: 2018 update on diagnosis, risk‐stratification and management. American journal of hematology. 2018;93(1):129-47.##Brück OE, Lallukka-Brück SE, Hohtari HR, Ianevski A, Ebeling FT, Kovanen PE, et al. Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS. Blood Cancer Discov. 2021;2(3):238-49.##Syrykh C, Abreu A, Amara N, Siegfried A, Maisongrosse V, Frenois FX, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digit Med. 2020;3:63.##Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, et al. Author Correction: Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer. 2020;1(11):1129.##Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559-67.##Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 2021;22(1):132-41.##Courtiol P, Maussion C, Moarii M, Pronier E, Pilcer S, Sefta M, et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat Med. 2019;25(10):1519-25.##Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8(1):3395.##Timmerman L. What's in a name? A lot, when it comes to'precision medicine'. Xconomy. 2013.##Johannet P, Coudray N, Donnelly DM, Jour G, Illa-Bochaca I, Xia Y, et al. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clin Cancer Res. 2021;27(1):131-40.##Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Arnon L, et al. Author Correction: Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2020;26(7):1147.##Qiang M, Li C, Sun Y, Sun Y, Ke L, Xie C, et al. A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma. J Natl Cancer Inst. 2021;113(5):606-15.##Peng H, Dong D, Fang MJ, Li L, Tang LL, Chen L, et al. Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma. Clin Cancer Res. 2019;25(14):4271-9.##Huynh BQ, Antropova N, Giger ML, editors. Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning. Medical imaging 2017: computer-aided diagnosis; 2017: SPIE.##Ha R, Chin C, Karcich J, Liu MZ, Chang P, Mutasa S, et al. Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset. J Digit Imaging. 2019;32(5):693-701.##Shi L, Zhang Y, Nie K, Sun X, Niu T, Yue N, et al. Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI. Magn Reson Imaging. 2019;61:33-40.##Sun C, Li B, Wei G, Qiu W, Li D, Li X, et al. Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer. Comput Methods Programs Biomed. 2022;221:106914.##Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):e1-e36.##Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, et al. Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys. J Digit Imaging. 2017;30(4):442-8.##Hu P, Wu F, Peng J, Liang P, Kong D. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol. 2016;61(24):8676-98.##Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017;44(12):6377-89.##van Rooij W, Dahele M, Ribeiro Brandao H, Delaney AR, Slotman BJ, Verbakel WF. Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation. Int J Radiat Oncol Biol Phys. 2019;104(3):677-84.##Liang S, Tang F, Huang X, Yang K, Zhong T, Hu R, et al. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. Eur Radiol. 2019;29(4):1961-7.##Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44(2):547-57.##Lin L, Dou Q, Jin YM, Zhou GQ, Tang YQ, Chen WL, et al. Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma. Radiology. 2019;291(3):677-86.##Fan J, Wang J, Chen Z, Hu C, Zhang Z, Hu W. Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique. Med Phys. 2019;46(1):370-81.##Ibragimov B, Toesca D, Chang D, Yuan Y, Koong A, Xing L. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys. 2018;45(10):4763-74.##Valdes G, Solberg TD, Heskel M, Ungar L, Simone CB, 2nd. Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy. Phys Med Biol. 2016;61(16):6105-20.##Maspero M, Savenije MHF, Dinkla AM, Seevinck PR, Intven MPW, Jurgenliemk-Schulz IM, et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol. 2018;63(18):185001.##Liu Y, Lei Y, Wang Y, Wang T, Ren L, Lin L, et al. MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method. Phys Med Biol. 2019;64(14):145015.##Madesta F, Sentker T, Gauer T, Werner R. Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction. Med Phys. 2020;47(11):5619-31.##Terpstra ML, Maspero M, d'Agata F, Stemkens B, Intven MPW, Lagendijk JJW, et al. Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy. Phys Med Biol. 2020;65(15):155015.## ##

Oncology in the modern era: Artificial Intelligence is reshaping cancer diagnosis, prognosis and treatment 2 2 The field of cancer research has been profoundly impacted by the utilization of artificial intelligence (AI), particularly through the analysis of medical records encompassing genomics, transcriptomics, proteomics, and imaging data. Subdomains of AI, such as machine learning (ML) and deep learning (DL), possess the capability to analyze intricate patterns within these records. This allows for groundbreaking advancements in cancer diagnosis, prognosis, and treatment by extracting valuable insights from sources such as histology and radiology imaging. The integration of AI-based models has led to improved prediction, diagnosis, and even treatment of various types of cancer, resulting in enhanced performance within the field of oncology. However, AI also faces challenges including ethical and legal considerations, data quality and accessibility, and issues pertaining to model interpretability. It is crucial to develop and evaluate AI-based systems in collaboration with clinicians and researchers to ensure their safety, reliability, and validity in cancer research.   13 41 2023/06/252023/06/242023/07/1 1402/4/10 2023/08/172023/08/132023/08/1 1402/5/10 Mahda Delshad Mahda Delshad Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Department of Laboratory Sciences, School of Allied Medical Sciences, Zanjan University of Medical Sciences, Zanjan, Iran. Delshad.mahda@gmail.com Mohammad Amin Omrani Mohammad Amin Omrani Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran. Aminomrani1997@gmail.com Atieh Pourbagheri-Sigaroodi Atieh Pourbagheri-Sigaroodi Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. a.pourbagherisigaroodi@gmail.com Davood Bashash Davood Bashash Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. D.bashash@sbmu.ac.ir Artificial intelligence (AI) Machine learning Deep learning Cancer McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine. 2006;27(4):12-.##AI vs Machine Learning vs Deep Learning: Know the Differences Last updated on Feb 23, 2023 [Available from: https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/ai-vs-machine-learning-vs-deep-learning.##Artificial Intelligence vs. Machine Learning vs. Deep Learning: What's the Difference? 2023 [Available from: https://builtin.com/artificial-intelligence/ai-vs-machine-learning.##Güneş ED, Yaman H, Cekyay B, Verter V. Matching patient and physician preferences in designing a primary care facility network. Journal of the Operational Research Society. 2014;65:483-96.##Ekins S. The next era: deep learning in pharmaceutical research. Pharmaceutical research. 2016;33(11):2594-603.##Jing Y, Bian Y, Hu Z, Wang L, Xie X-QS. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. The AAPS journal. 2018;20:1-10.##https://doi.org/10.1208/s12248-018-0243-4##Papachristou I, Bosanquet N. Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia. Journal of Public Health Policy. 2020;41:28-38.##Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama. 2016;316(22):2402-10.##Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer letters. 2020;471:61-71.##Patel SK, George B, Rai V. Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Frontiers in Pharmacology. 2020;11:1177.##Song H, Ruan C, Xu Y, Xu T, Fan R, Jiang T, Cao M, Song J. Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model. Experimental Biology and Medicine. 2022;247(11):898-909.##Griffith M, Miller CA, Griffith OL, Krysiak K, Skidmore ZL, Ramu A, Walker JR, Dang HX, Trani L, Larson DE. Optimizing cancer genome sequencing and analysis. Cell systems. 2015;1(3):210-23.##Gaublomme JT, Li B, McCabe C, Knecht A, Yang Y, Drokhlyansky E, Van Wittenberghe N, Waldman J, Dionne D, Nguyen L. Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nature communications. 2019;10(1):2907.##Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, Schüffler PJ, Grolimund D, Buhmann JM, Brandt S. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature methods. 2014;11(4):417-22.##Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202-14.##Moffitt JR, Hao J, Wang G, Chen KH, Babcock HP, Zhuang X. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proceedings of the National Academy of Sciences. 2016;113(39):11046-51.##Wang X, Yu G, Yan Z, Wan L, Wang W, Lizhen LCC. Lung cancer subtype diagnosis by fusing image-genomics data and hybrid deep networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2021.##Vanderbilt CM, Bowman AS, Middha S, Petrova-Drus K, Tang Y-W, Chen X, Wang Y, Chang J, Rekhtman N, Busam KJ. Defining novel DNA virus-tumor associations and genomic correlates using prospective clinical tumor/normal matched sequencing data. The Journal of Molecular Diagnostics. 2022;24(5):515-28.##Buzdin A, Sorokin M, Garazha A, Sekacheva M, Kim E, Zhukov N, Wang Y, Li X, Kar S, Hartmann C, editors. Molecular pathway activation-new type of biomarkers for tumor morphology and personalized selection of target drugs. Seminars in Cancer Biology; 2018: Elsevier.##Supplitt S, Karpinski P, Sasiadek M, Laczmanska I. Current achievements and applications of transcriptomics in personalized cancer medicine. International Journal of Molecular Sciences. 2021;22(3):1422.##Montani F, Marzi MJ, Dezi F, Dama E, Carletti RM, Bonizzi G, Bertolotti R, Bellomi M, Rampinelli C, Maisonneuve P. miR-Test: a blood test for lung cancer early detection. Journal of the National Cancer Institute. 2015;107(6):djv063.##Nadal E, Truini A, Nakata A, Lin J, Reddy RM, Chang AC, Ramnath N, Gotoh N, Beer DG, Chen G. A novel serum 4-microRNA signature for lung cancer detection. Scientific reports. 2015;5(1):12464.##Warnat-Herresthal S, Perrakis K, Taschler B, Becker M, Baßler K, Beyer M, Günther P, Schulte-Schrepping J, Seep L, Klee K. Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. Iscience. 2020;23(1).##Azzouz FB, Michel B, Lasla H, Gouraud W, François A-F, Girka F, Lecointre T, Guérin-Charbonnel C, Juin PP, Campone M. Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches. Computers in Biology and Medicine. 2021;129:104171.##Alkhateeb A, Rezaeian I, Singireddy S, Cavallo-Medved D, Porter LA, Rueda L. Transcriptomics signature from next-generation sequencing data reveals new transcriptomic biomarkers related to prostate cancer. Cancer informatics. 2019;18:1176935119835522.##Masuda T, Mori A, Ito S, Ohtsuki S. Quantitative and targeted proteomics-based identification and validation of drug efficacy biomarkers. Drug Metabolism and Pharmacokinetics. 2021;36:100361.##Islam Khan MZ, Tam SY, Law HKW. Advances in high throughput proteomics profiling in establishing potential biomarkers for gastrointestinal cancer. Cells. 2022;11(6):973.##Li N, Long Y, Fan X, Liu H, Li C, Chen L, Wang Z. Proteomic analysis of differentially expressed proteins in hepatitis B virus-related hepatocellular carcinoma tissues. Journal of Experimental & Clinical Cancer Research. 2009;28:1-10.##Murata M, Matsuzaki K, Yoshida K, Sekimoto G, Tahashi Y, Mori S, Uemura Y, Sakaida N, Fujisawa J, Seki T. Hepatitis B virus X protein shifts human hepatic transforming growth factor (TGF)‐β signaling from tumor suppression to oncogenesis in early chronic hepatitis B. Hepatology. 2009;49(4):1203-17.##Wu Z, Doondeea JB, Gholami AM, Janning MC, Lemeer S, Kramer K, Eccles SA, Gollin SM, Grenman R, Walch A. Quantitative chemical proteomics reveals new potential drug targets in head and neck cancer. Molecular & Cellular Proteomics. 2011;10(12).##Nambu M, Masuda T, Ito S, Kato K, Kojima T, Daiko H, Ito Y, Honda K, Ohtsuki S. Leucine-rich alpha-2-glycoprotein 1 in serum is a possible biomarker to predict response to preoperative chemoradiotherapy for esophageal cancer. Biological and Pharmaceutical Bulletin. 2019;42(10):1766-71.##Huang X, Zeng Y, Xing X, Zeng J, Gao Y, Cai Z, Xu B, Liu X, Huang A, Liu J. Quantitative proteomics analysis of early recurrence/metastasis of huge hepatocellular carcinoma following radical resection. Proteome science. 2014;12:1-14.##https://doi.org/10.1186/1477-5956-12-1##Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, Rajeeve V, Fitzgibbon J, Travers J, Britton D. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nature communications. 2021;12(1):1850.##Farinella F, Merone M, Bacco L, Capirchio A, Ciccozzi M, Caligiore D. Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers. Scientific Reports. 2022;12(1):3041.##Tong L, Wu H, Wang MD. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer. Methods. 2021;189:74-85.##Lewis JE, Kemp ML. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance. Nature Communications. 2021;12(1):2700.##Nicora G, Vitali F, Dagliati A, Geifman N, Bellazzi R. Integrated multi-omics analyses in oncology: a review of machine learning methods and tools. Frontiers in oncology. 2020;10:1030.##Wang Y, Yang Y, Chen S, Wang J. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration. Briefings in Bioinformatics. 2021;22(5):bbab048.##Ma T, Zhang A. Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE). BMC genomics. 2019;20:1-11.##Wang Z, Zhao Y, Shen X, Zhao Y, Zhang Z, Yin H, Zhao X, Liu H, Shi Q. Single-cell genomics-based molecular algorithm for early cancer detection. Analytical Chemistry. 2022;94(5):2607-14.##Poirion OB, Jing Z, Chaudhary K, Huang S, Garmire LX. DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome medicine. 2021;13(1):1-15.##Araújo ALD, Arboleda LPA, Palmier NR, Fonseca JM, de Pauli Paglioni M, Gomes-Silva W, Ribeiro ACP, Brandao TB, Simonato LE, Speight PM. The performance of digital microscopy for primary diagnosis in human pathology: a systematic review. Virchows Archiv. 2019;474:269-87.##Hou Q, Bing Z-T, Hu C, Li M-Y, Yang K-H, Mo Z, Xie X-W, Liao J-L, Lu Y, Horie S. RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer. EBioMedicine. 2018;32:234-44.##Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nature reviews Clinical oncology. 2019;16(11):703-15.##Mukhopadhyay S, Feldman MD, Abels E, Ashfaq R, Beltaifa S, Cacciabeve NG, Cathro HP, Cheng L, Cooper K, Dickey GE. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (pivotal study). The American journal of surgical pathology. 2018;42(1):39.##Tosta TAA, de Faria PR, Neves LA, do Nascimento MZ. Color normalization of faded H;E-stained histological images using spectral matching. Computers in biology and medicine. 2019;111:103344.##Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE transactions on medical imaging. 2016;35(5):1196-206.##Martins J, Magalhães C, Rocha M, Osório NS. Machine learning-enhanced T cell neoepitope discovery for immunotherapy design. Cancer informatics. 2019;18:1176935119852081.##Araújo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Polónia A, Campilho A. Classification of breast cancer histology images using convolutional neural networks. PloS one. 2017;12(6):e0177544.##Wang L, Ding L, Liu Z, Sun L, Chen L, Jia R, Dai X, Cao J, Ye J. Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. British Journal of Ophthalmology. 2020;104(3):318-23.##Iizuka O, Kanavati F, Kato K, Rambeau M, Arihiro K, Tsuneki M. Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Scientific reports. 2020;10(1):1504.##Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity. 2013;39(1):11-26.##Koelzer VH, Sirinukunwattana K, Rittscher J, Mertz KD. Precision immunoprofiling by image analysis and artificial intelligence. Virchows Archiv. 2019;474:511-22.##Qaiser T, Mukherjee A, Reddy Pb C, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A. Her 2 challenge contest: a detailed assessment of automated her 2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology. 2018;72(2):227-38.##Win KP, Kitjaidure Y, Hamamoto K, Myo Aung T. Computer-assisted screening for cervical cancer using digital image processing of pap smear images. Applied Sciences. 2020;10(5):1800.##Leon F, Gelvez M, Jaimes Z, Gelvez T, Arguello H, editors. Supervised classification of histopathological images using convolutional neuronal networks for gastric cancer detection. 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA); 2019: IEEE.##Dov D, Kovalsky SZ, Cohen J, Range DE, Henao R, Carin L, editors. Thyroid cancer malignancy prediction from whole slide cytopathology images. Machine Learning for Healthcare Conference; 2019: PMLR.##Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature medicine. 2019;25(7):1054-6.##Abdous S, Abdollahzadeh R, Rohban MH. KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images. arXiv preprint arXiv:230408240. 2023.##Tabibu S, Vinod P, Jawahar C. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Scientific reports. 2019;9(1):10509.##Wu M, Yan C, Liu H, Liu Q, Yin Y. Automatic classification of cervical cancer from cytological images by using convolutional neural network. Bioscience reports. 2018;38(6):BSR20181769.##Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS one. 2019;14(3):e0214587.##Aprupe L, Litjens G, Brinker TJ, van der Laak J, Grabe N. Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks. PeerJ. 2019;7:e6335.##Swiderska-Chadaj Z, Pinckaers H, van Rijthoven M, Balkenhol M, Melnikova M, Geessink O, Manson Q, Sherman M, Polonia A, Parry J. Learning to detect lymphocytes in immunohistochemistry with deep learning. Medical image analysis. 2019;58:101547.##Paul PR, Bhowmik MK, Bhattacharjee D, editors. Automated cervical cancer detection using Pap smear images. Proceedings of Fourth International Conference on Soft Computing for Problem Solving: SocProS 2014, Volume 1; 2015: Springer.##Malathi M, Sinthia P. Brain tumour segmentation using convolutional neural network with tensor flow. Asian Pacific journal of cancer prevention: APJCP. 2019;20(7):2095.##Kalaiselvi T, Nagaraja P. A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique. International journal of imaging systems and technology. 2015;1(25):77-85.##Saha A, Harowicz MR, Wang W, Mazurowski MA. A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. Journal of cancer research and clinical oncology. 2018;144:799-807.##Brossard C, Lemasson B, Attyé A, De Busschère J-A, Payen J-F, Barbier EL, Grèze J, Bouzat P. Contribution of CT-scan analysis by artificial intelligence to the clinical care of TBI patients. Frontiers in Neurology. 2021;12:666875.##Kaheel H, Hussein A, Chehab A. AI-based image processing for COVID-19 detection in chest CT scan images. Frontiers in Communications and Networks. 2021;2:645040.##Godkhindi AM, Gowda RM, editors. Automated detection of polyps in CT colonography images using deep learning algorithms in colon cancer diagnosis. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS); 2017: IEEE.##Chillakuru YR, Kranen K, Doppalapudi V, Xiong Z, Fu L, Heydari A, Sheth A, Seo Y, Vu T, Sohn JH. High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels. BMC medical imaging. 2021;21:1-13.##Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific reports. 2018;8(1):15497.##Shen Y-T, Chen L, Yue W-W, Xu H-X. Artificial intelligence in ultrasound. European Journal of Radiology. 2021;139:109717.##Poudel P, Illanes A, Sheet D, Friebe M. Evaluation of commonly used algorithms for thyroid ultrasound images segmentation and improvement using machine learning approaches. Journal of healthcare engineering. 2018;2018.##Dong H, Yang G, Liu F, Mo Y, Guo Y, editors. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11-13, 2017, Proceedings 21; 2017: Springer.##Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal. 2018;3(1):68-71.##Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM, Ahmed KT, Singh BC, Miah MS. Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data and Cognitive Computing. 2019;3(2):27.##Le T-N, Huynh HT. Liver tumor segmentation from MR images using 3D fast marching algorithm and single hidden layer feedforward neural network. BioMed research international. 2016;2016.##Liu S, Zheng H, Feng Y, Li W, editors. Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. Medical imaging 2017: computer-aided diagnosis; 2017: SPIE.##Yoo S, Gujrathi I, Haider MA, Khalvati F. Prostate cancer detection using deep convolutional neural networks. Scientific reports. 2019;9(1):19518.##Skalski A, Jakubowski J, Drewniak T, editors. Kidney tumor segmentation and detection on computed tomography data. 2016 IEEE International Conference on Imaging Systems and Techniques (IST); 2016: IEEE.##Han S, Hwang SI, Lee HJ. The classification of renal cancer in 3-phase CT images using a deep learning method. Journal of digital imaging. 2019;32:638-43.##Senthil Kumar K, Venkatalakshmi K, Karthikeyan K. Lung cancer detection using image segmentation by means of various evolutionary algorithms. Computational and mathematical methods in medicine. 2019;2019.##Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T, editors. Automatic lung cancer prediction from chest X-ray images using the deep learning approach. 2018 11th biomedical engineering international conference (BMEiCON); 2018: IEEE.##Chiu H-Y, Peng RH-T, Lin Y-C, Wang T-W, Yang Y-X, Chen Y-Y, Wu M-H, Shiao T-H, Chao H-S, Chen Y-M. Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images. Biomedicines. 2022;10(11):2839.##Guan Q, Wang Y, Du J, Qin Y, Lu H, Xiang J, Wang F. Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of translational medicine. 2019;7(7).##Nugroho HA, Frannita EL, Ardiyanto I, Choridah L. Computer aided diagnosis for thyroid cancer system based on internal and external characteristics. Journal of King Saud University-Computer and Information Sciences. 2021;33(3):329-39.##Ragab DA, Sharkas M, Marshall S, Ren J. Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ. 2019;7:e6201.##Bidard F-C, Mathiot C, Delaloge S, Brain E, Giachetti S, De Cremoux P, Marty M, Pierga J-Y. Single circulating tumor cell detection and overall survival in nonmetastatic breast cancer. Annals of Oncology. 2010;21(4):729-33.##Kaur P, Singh G, Kaur P. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics in Medicine Unlocked. 2019;16:100151.##https://doi.org/10.1016/j.imu.2019.01.001##Patil RS, Biradar N. Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network. Evolutionary intelligence. 2021;14:1459-74.##Trang NTH, Long KQ, An PL, Dang TN. Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records. Diagnostics. 2023;13(3):346.##Borrelli P, Ly J, Kaboteh R, Ulén J, Enqvist O, Trägårdh E, Edenbrandt L. AI-based detection of lung lesions in [18F] FDG PET-CT from lung cancer patients. EJNMMI physics. 2021;8(1):1-11.##Huang B, Sollee J, Luo Y-H, Reddy A, Zhong Z, Wu J, Mammarappallil J, Healey T, Cheng G, Azzoli C. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT. EBioMedicine. 2022;82.##Alongi P, Stefano A, Comelli A, Spataro A, Formica G, Laudicella R, Lanzafame H, Panasiti F, Longo C, Midiri F. Artificial intelligence applications on restaging [18F] FDG PET/CT in metastatic colorectal cancer: A preliminary report of morpho-functional radiomics classification for prediction of disease outcome. Applied Sciences. 2022;12(6):2941.##Yamada M, Saito Y, Imaoka H, Saiko M, Yamada S, Kondo H, Takamaru H, Sakamoto T, Sese J, Kuchiba A. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific reports. 2019;9(1):14465.##Zhang R, Zheng Y, Mak TWC, Yu R, Wong SH, Lau JY, Poon CC. Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE journal of biomedical and health informatics. 2016;21(1):41-7.##Shin Y, Qadir HA, Aabakken L, Bergsland J, Balasingham I. Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access. 2018;6:40950-62.##Khryashchev V, Stepanova O, Lebedev A, Kashin S, Kuvaev R, editors. Deep learning for gastric pathology detection in endoscopic images. Proceedings of the 3rd International Conference on Graphics and Signal Processing; 2019.##Shibata T, Teramoto A, Yamada H, Ohmiya N, Saito K, Fujita H. Automated detection and segmentation of early gastric cancer from endoscopic images using mask R-CNN. Applied Sciences. 2020;10(11):3842.##Sakai Y, Takemoto S, Hori K, Nishimura M, Ikematsu H, Yano T, Yokota H, editors. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018: IEEE.##Mane S, Shinde S, editors. A method for melanoma skin cancer detection using dermoscopy images. 2018 Fourth international conference on computing communication control and automation (ICCUBEA); 2018: IEEE.##Udrea A, Mitra GD, editors. Generative adversarial neural networks for pigmented and non-pigmented skin lesions detection in clinical images. 2017 21st international conference on control systems and computer science (CSCS); 2017: IEEE.##Hasan M, Barman SD, Islam S, Reza AW, editors. Skin cancer detection using convolutional neural network. Proceedings of the 2019 5th international conference on computing and artificial intelligence; 2019.##Marchetti MA, Cowen EA, Kurtansky NR, Weber J, Dauscher M, DeFazio J, Deng L, Dusza SW, Haliasos H, Halpern AC. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). NPJ Digital Medicine. 2023;6(1):127.##Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, Malik K, Raza S, Abbas A, Pezzani R. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer cell international. 2021;21(1):1-11.##Bibault J-E, Chang DT, Xing L. Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884-9.##Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke and vascular neurology. 2019;4(4).##Liu A-A, Zhai Y, Xu N, Nie W, Li W, Zhang Y. Region-aware image captioning via interaction learning. IEEE Transactions on Circuits and Systems for Video Technology. 2021;32(6):3685-96.##Yang S, Li Q, Li W, Li X, Liu A-A. Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Transactions on Circuits and Systems for Video Technology. 2022;32(11):8037-50.##Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlentherapie und Onkologie. 2020;196:879-87.##Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlentherapie und Onkologie. 2020;196:888-99.##Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlentherapie und Onkologie. 2020;196:856-67.##Danaee P, Ghaeini R, Hendrix DA, editors. A deep learning approach for cancer detection and relevant gene identification. Pacific symposium on biocomputing 2017; 2017: World Scientific.##Bębas E, Borowska M, Derlatka M, Oczeretko E, Hładuński M, Szumowski P, Mojsak M. Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis. Biomedical Signal Processing and Control. 2021;66:102446.##Jhajharia S, Varshney HK, Verma S, Kumar R, editors. A neural network based breast cancer prognosis model with PCA processed features. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI); 2016: IEEE.##Ching T, Zhu X, Garmire LX. Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS computational biology. 2018;14(4):e1006076.##Bomane A, Gonçalves A, Ballester PJ. Paclitaxel response can be predicted with interpretable multi-variate classifiers exploiting DNA-methylation and miRNA data. Frontiers in genetics. 2019;10:1041.##Kuo R-J, Huang M-H, Cheng W-C, Lin C-C, Wu Y-H. Application of a two-stage fuzzy neural network to a prostate cancer prognosis system. Artificial intelligence in medicine. 2015;63(2):119-33.##Zhang S, Xu Y, Hui X, Yang F, Hu Y, Shao J, Liang H, Wang Y. Improvement in prediction of prostate cancer prognosis with somatic mutational signatures. Journal of Cancer. 2017;8(16):3261.##Lind AP, Anderson PC. Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties. PloS one. 2019;14(7):e0219774.##Paik ES, Lee J-W, Park J-Y, Kim J-H, Kim M, Kim T-J, Choi CH, Kim B-G, Bae D-S, Seo SW. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. Journal of gynecologic oncology. 2019;30(4).##McDonald JF. Back to the future-The integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics. Gynecologic oncology. 2018;149(2):230-1.##Li Q, Qi L, Feng Q-X, Liu C, Sun S-W, Zhang J, Yang G, Ge Y-Q, Zhang Y-D, Liu X-S. Machine learning-based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric cancer. Clinical and Translational Gastroenterology. 2019;10(10).##Taninaga J, Nishiyama Y, Fujibayashi K, Gunji T, Sasabe N, Iijima K, Naito T. Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. Scientific reports. 2019;9(1):12384.##Liu C, Qi L, Feng Q-X, Sun S-W, Zhang Y-D, Liu X-S. Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection. Abdominal Radiology. 2019;44:3019-29.##Stanzione A, Cuocolo R, Del Grosso R, Nardiello A, Romeo V, Travaglino A, Raffone A, Bifulco G, Zullo F, Insabato L. Deep myometrial infiltration of endometrial cancer on MRI: A radiomics-powered machine learning pilot study. Academic radiology. 2021;28(5):737-44.##Günakan E, Atan S, Haberal AN, Küçükyıldız İA, Gökçe E, Ayhan A. A novel prediction method for lymph node involvement in endometrial cancer: machine learning. International Journal of Gynecologic Cancer. 2019;29(2).##Beck JT, Rammage M, Jackson GP, Preininger AM, Dankwa-Mullan I, Roebuck MC, Torres A, Holtzen H, Coverdill SE, Williamson MP. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO clinical cancer informatics. 2020;4:50-9.##Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology. 2019;16(7):391-403.##Nascimento AC, Prudêncio RB, Costa IG. A drug-target network-based supervised machine learning repurposing method allowing the use of multiple heterogeneous information sources. Computational Methods for Drug Repurposing. 2019:281-9.##Sharma A, Rani R. Ensembled machine learning framework for drug sensitivity prediction. IET Systems Biology. 2020;14(1):39-46.##Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M. Applications of machine learning in drug discovery and development. Nature reviews Drug discovery. 2019;18(6):463-77.##Xia X, Gong J, Hao W, Yang T, Lin Y, Wang S, Peng W. Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scan. Frontiers in oncology. 2020;10:418.##Baskin II. The power of deep learning to ligand-based novel drug discovery. Expert opinion on drug discovery. 2020;15(7):755-64.##Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8(7):10883.##Juwara L, Arora N, Gornitsky M, Saha-Chaudhuri P, Velly AM. Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning. International journal of medical informatics. 2020;141:104170.##Kenngott H, Wagner M, Nickel F, Wekerle A, Preukschas A, Apitz M, Schulte T, Rempel R, Mietkowski P, Wagner F. Computer-assisted abdominal surgery: new technologies. Langenbeck's archives of surgery. 2015;400:273-81.##Kingham TP, Scherer MA, Neese BW, Clements LW, Stefansic JD, Jarnagin WR. Image-guided liver surgery: intraoperative projection of computed tomography images utilizing tracked ultrasound. Hpb. 2012;14(9):594-603.##Okada T, Kawada K, Sumii A, Itatani Y, Hida K, Hasegawa S, Sakai Y. Stereotactic Navigation for Rectal Surgery: Comparison of 3-Dimensional C-Arm− Based Registration to Paired-Point Registration. Diseases of the Colon & Rectum. 2020;63(5):693-700.##Mahmud S, Abbas TO, Mushtak A, Prithula J, Chowdhury ME. Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata. Cancers. 2023;15(12):3189.##Satava RM. Robotic surgery: from past to future-a personal journey. Surgical Clinics. 2003;83(6):1491-500.##Cao L, Yang Z, Qi L, Chen M. Robot-assisted and laparoscopic vs open radical prostatectomy in clinically localized prostate cancer: perioperative, functional, and oncological outcomes: A Systematic review and meta-analysis. Medicine. 2019;98(22).##Ngiam KY, Khor W. Big data and machine learning algorithms for health-care delivery. The Lancet Oncology. 2019;20(5):e262-e73.##Chen G, Tsoi A, Xu H, Zheng WJ. Predict effective drug combination by deep belief network and ontology fingerprints. Journal of biomedical informatics. 2018;85:149-54.##Levine MN, Alexander G, Sathiyapalan A, Agrawal A, Pond G. Learning health system for breast cancer: pilot project experience. JCO clinical cancer informatics. 2019;3:1-11.##Smaïl-Tabbone M, Rance B. Contributions from the 2019 Literature on Bioinformatics and Translational Informatics. Yearbook of Medical Informatics. 2020;29(01):188-92.##Pantuck AJ, Lee DK, Kee T, Wang P, Lakhotia S, Silverman MH, Mathis C, Drakaki A, Belldegrun AS, Ho CM. Modulating BET bromodomain inhibitor ZEN‐3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE. AI, an artificial intelligence platform. Advanced Therapeutics. 2018;1(6):1800104.##Gulhan DC, Lee JJ-K, Melloni GE, Cortes-Ciriano I, Park PJ. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nature genetics. 2019;51(5):912-9.##Tang X, Huang Y, Lei J, Luo H, Zhu X. The single-cell sequencing: new developments and medical applications. Cell & bioscience. 2019;9:1-9.##Peng H, Dong D, Fang M-J, Li L, Tang L-L, Chen L, Li W-F, Mao Y-P, Fan W, Liu L-Z. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clinical Cancer Research. 2019;25(14):4271-9.##Staffurth J. A review of the clinical evidence for intensity-modulated radiotherapy. Clinical oncology. 2010;22(8):643-57.##Otto K. Volumetric modulated arc therapy: IMRT in a single gantry arc. Medical physics. 2008;35(1):310-7.##Crooks S, Wu X, Takita C, Watzich M, Xing L. Aperture modulated arc therapy. Physics in medicine & biology. 2003;48(10):1333.##Palma DA, Olson R, Harrow S, Gaede S, Louie AV, Haasbeek C, Mulroy L, Lock M, Rodrigues GB, Yaremko BP. Stereotactic ablative radiotherapy versus standard of care palliative treatment in patients with oligometastatic cancers (SABR-COMET): a randomised, phase 2, open-label trial. The Lancet. 2019;393(10185):2051-8.##Timmerman RD, Kavanagh BD, Cho LC, Papiez L, Xing L. Stereotactic body radiation therapy in multiple organ sites. Journal of Clinical Oncology. 2007;25(8):947-52.##Langen KM, Jones DT. Organ motion and its management. International Journal of Radiation Oncology* Biology* Physics. 2001;50(1):265-78.##van Lin ENT, van der Vight LP, Witjes JA, Huisman HJ, Leer JW, Visser AG. The effect of an endorectal balloon and off-line correction on the interfraction systematic and random prostate position variations: a comparative study. International Journal of Radiation Oncology* Biology* Physics. 2005;61(1):278-88.##Shen L, Zhao W, Xing L. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning. Nature biomedical engineering. 2019;3(11):880-8.##Zhao W, Shen L, Islam MT, Qin W, Zhang Z, Liang X, Zhang G, Xu S, Li X. Artificial intelligence in image-guided radiotherapy: a review of treatment target localization. Quantitative Imaging in Medicine and Surgery. 2021;11(12):4881.##Fiorino C, Guckenberger M, Schwarz M, van der Heide UA, Heijmen B. Technology‐driven research for radiotherapy innovation. Molecular oncology. 2020;14(7):1500-13.##Lou B, Doken S, Zhuang T, Wingerter D, Gidwani M, Mistry N, Ladic L, Kamen A, Abazeed ME. An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. The Lancet Digital Health. 2019;1(3):e136-e47.##Lin H, Xiao H, Dong L, Teo KB-K, Zou W, Cai J, Li T. Deep learning for automatic target volume segmentation in radiation therapy: a review. Quantitative Imaging in Medicine and Surgery. 2021;11(12):4847.##Cha KH, Hadjiiski L, Chan H-P, Weizer AZ, Alva A, Cohan RH, Caoili EM, Paramagul C, Samala RK. Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Scientific reports. 2017;7(1):8738.##Babier A, Boutilier JJ, McNiven AL, Chan TC. Knowledge‐based automated planning for oropharyngeal cancer. Medical physics. 2018;45(7):2875-83.##Jabbari P, Rezaei N. Artificial intelligence and immunotherapy. Expert Review of Clinical Immunology. 2019;15(7):689-91.##Trebeschi S, Drago S, Birkbak N, Kurilova I, Cǎlin A, Pizzi AD, Lalezari F, Lambregts D, Rohaan M, Parmar C. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Annals of Oncology. 2019;30(6):998-1004.##Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. The Lancet Oncology. 2018;19(9):1180-91.##Bulik-Sullivan B, Busby J, Palmer CD, Davis MJ, Murphy T, Clark A, Busby M, Duke F, Yang A, Young L. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nature biotechnology. 2019;37(1):55-63.##Boehm KM, Bhinder B, Raja VJ, Dephoure N, Elemento O. Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome. BMC bioinformatics. 2019;20:1-11.##Ge P, Wang W, Li L, Zhang G, Gao Z, Tang Z, Dang X, Wu Y. Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of colorectal cancer. Biomedicine & Pharmacotherapy. 2019;118:109228.##Schmidt J, Guillaume P, Dojcinovic D, Karbach J, Coukos G, Luescher I. In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes. Journal of Biological Chemistry. 2017;292(28):11840-9.##Tosolini M, Pont F, Poupot M, Vergez F, Nicolau-Travers M-L, Vermijlen D, Sarry J-E, Dieli F, Fournié J-J. Assessment of tumor-infiltrating TCRV γ 9V δ 2 γδ lymphocyte abundance by deconvolution of human cancers microarrays. Oncoimmunology. 2017;6(3):e1284723.##Thurtle DR, Greenberg DC, Lee LS, Huang HH, Pharoah PD, Gnanapragasam VJ. Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model. PLoS medicine. 2019;16(3):e1002758.##Feng H, Gu Z-Y, Li Q, Liu Q-H, Yang X-Y, Zhang J-J. Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. Journal of ovarian research. 2019;12:1-9.##Qian D, Liu H, Wang X, Ge J, Luo S, Patz Jr EF, Moorman PG, Su L, Shen S, Christiani DC. Potentially functional genetic variants in the complement‐related immunity gene‐set are associated with non‐small cell lung cancer survival. International journal of cancer. 2019;144(8):1867-76.##Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities. Journal of pathology informatics. 2018;9(1):38.##Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine. 2019;17:1-9.##Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and structural biotechnology journal. 2020;18:2300-11.##Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The lancet digital health. 2019;1(6):e271-e97.##Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World journal of gastroenterology. 2019;25(14):1666.##Delclaux C. No need for pulmonologists to interpret pulmonary function tests. Eur Respiratory Soc; 2019.##Kumar Y, Gupta S, Singla R, Hu Y-C. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Archives of Computational Methods in Engineering. 2021:1-28.##Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. British Journal of Cancer. 2022;126(1):4-9.##Yang F, Darsey JA, Ghosh A, Li H-Y, Yang MQ, Wang S. Artificial intelligence and cancer drug development. Recent Patents on Anti-Cancer Drug Discovery. 2022;17(1):2-8.##You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduction and Targeted Therapy. 2022;7(1):156.##Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenge. Journal of medical Internet research. 2019;21(7):e13659.## ##

How artificial intelligence is revolutionizing precision medicine and drug discovery? 2 2 Traditionally, medical treatments have been developed using a standardized approach, where identical treatment protocols are administered to all patients with a particular disease or condition. Precision medicine represents a groundbreaking approach to healthcare that centers on customizing medical treatments and interventions to individual patients based on their unique environmental factors, lifestyles, and molecular profiles. This approach has been shown to enhance the success rates of clinical trials and expedite drug approvals. By harnessing vast amounts of data and sophisticated algorithms, artificial intelligence (AI) has the potential to transform precision medicine and drug discovery. AI can offer valuable insights into all facets of precision medicine, including expediting the development of new therapies, optimizing clinical trials, facilitating accurate diagnoses, guiding treatment decisions, and monitoring patients. In this review, we endeavor to explore the ways in which AI will impact the various aspects of precision medicine and drug discovery. 42 59 2023/06/252023/06/242023/07/12023/07/1 1402/4/10 2023/08/172023/08/132023/08/12023/08/29 1402/6/7 Amir-Mohammad Yousefi Amir-Mohammad Yousefi Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. amirmm.yousefi@yahoo.com Ashkan Zandi Ashkan Zandi School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. zandi@gatecedu Fatemeh Shojaeian Fatemeh Shojaeian Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA. fshojae1@jhmi.edu Mahmood Reza Marzban Mahmood Reza Marzban School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. mmarzban3@gatech.edu Mohammad Reza Tavakol Mohammad Reza Tavakol School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. tavak@gatech.edu Hamed Abiri Hamed Abiri School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. habiri3@gatech.edu Mahsa Hojabri Mahsa Hojabri Institute of Human Virology, School of Medicine, University of Maryland, Baltimore, MD, USA. mahsa.hojabri@ihv.umaryland.edu Ghazal Kaviani Ghazal Kaviani School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. kaviani3@gatech.edu Sam Pournezhad Sam Pournezhad Marcus Stroke and Neuroscience Center, Grady Memorial Hospital, Atlanta, GA, 30303, USA. spournezhad@gmh.edu Artificial intelligence in medicine Precision medicine Drug discovery Machine learning Clinical trials optimization ## ##

ChatGPT in medicine: Opportunity and challenges 2 2 Chat generative pre-trained transformer (GPT) is a large language model (LLM) artificial intelligence (AI). Indeed, ChatGPT is a chatbot able to participate in a conversation by pretending to be a human. ChatGPT is able to write convincing academic texts which are hard to be distinguished from a human-written manuscript. In the medical context, ChatGPT demonstrated its ability to write abstracts and texts related to the given questions. It could answer medical questions whether they are asked by students or researchers. ChatGPT is able to enhance the knowledge of students by designing tests and answering personalized questions, consequently reducing the burden on teachers. This AI system can participate in healthcare programs by providing information for patients and acting as the connector between patients and healthcare providers. Also, it could serve as a translator and a text generator for patients who speak a different language or those who have speech difficulties. ChatGPT is also able to provide and categorize medical information necessary for healthcare providers and physicians. Nonetheless, the major concern is the level of reliability of generated data. In some cases, ChatGPT produced misleading information and fake citations which warned medical researchers. Worryingly, these false data could distract the process of treatments and or the projects of medical researchers. Regarding the inevitable necessity of AI utilization in the medical field, strict criteria should be enforced in order to improve the efficacy and reduce the safety of the application of any AI chatbot like ChatGPT.   60 67 2023/06/252023/06/242023/07/12023/07/12023/05/26 1402/3/5 2023/08/172023/08/132023/08/12023/08/292023/07/17 1402/4/26 Mohammad-Javad Sanaei Mohammad-Javad Sanaei Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran javadsanaei137@gmail.com Mehrnaz Sadat Ravari Mehrnaz Sadat Ravari Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran mehrnazravari1991@gmail.comjavadsanaei137@gmail.com Hassan Abolghasemi Hassan Abolghasemi Pediatric Congenital Hematologic Disorders Research Center, Mofid Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran h.abolghasemi.ha@gmail.com ChatGPT Large language model (LLM) Artificial intelligence (AI) Medical text Healthcare Vellido A. Societal Issues Concerning the Application of Artificial Intelligence in Medicine. Kidney Dis (Basel). 2019;5(1):11-7.##Cole M, Cant C, Ustek Spilda F, Graham M. Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at Work. Front Artif Intell. 2022;5:869114.##Korteling JEH, van de Boer-Visschedijk GC, Blankendaal RAM, Boonekamp RC, Eikelboom AR. Human- versus Artificial Intelligence. Front Artif Intell. 2021;4:622364.##Floridi L, Chiriatti M. GPT-3: Its nature, scope, limits, and consequences. Minds and Machines. 2020;30:681-94.##Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D. Emergent abilities of large language models. arXiv preprint arXiv:220607682. 2022.##OpenAI. Introducing ChatGPT 2022 [Available from: https://openai.com/blog/chatgpt.##OpenAI. GPT-4 is OpenAI's most advanced system, producing safer and more useful responses 2023 [Available from: https://openai.com/product/gpt-4.##Hutson M. Could AI help you to write your next paper? Nature. 2022;611(7934):192-3.##Vaishya R, Misra A, Vaish A. ChatGPT: Is this version good for healthcare and research? Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2023:102744.##Nature. AI bot ChatGPT writes smart essays - should professors worry? 2022 [Available from: https://www.nature.com/articles/d41586-022-04397-7.##Nature. ChatGPT listed as author on research papers: many scientists disapprove 2023 [Available from: https://www.nature.com/articles/d41586-023-00107-z.##Furie RA, van Vollenhoven RF, Kalunian K, Navarra S, Romero-Diaz J, Werth VP, Huang X, Clark G, Carroll H, Meyers A, Musselli C, Barbey C, Franchimont N. Trial of Anti-BDCA2 Antibody Litifilimab for Systemic Lupus Erythematosus. N Engl J Med. 2022;387(10):894-904.##Hugosson J, Månsson M, Wallström J, Axcrona U, Carlsson SV, Egevad L, Geterud K, Khatami A, Kohestani K, Pihl CG, Socratous A, Stranne J, Godtman RA, Hellström M. Prostate Cancer Screening with PSA and MRI Followed by Targeted Biopsy Only. N Engl J Med. 2022;387(23):2126-37.##Devos D, Labreuche J, Rascol O, Corvol JC, Duhamel A, Guyon Delannoy P, Poewe W, Compta Y, Pavese N, Růžička E, Dušek P, Post B, Bloem BR, Berg D, Maetzler W, Otto M, Habert MO, Lehericy S, Ferreira J, Dodel R, Tranchant C, Eusebio A, Thobois S, Marques AR, Meissner WG, Ory-Magne F, Walter U, de Bie RMA, Gago M, Vilas D, Kulisevsky J, Januario C, Coelho MVS, Behnke S, Worth P, Seppi K, Ouk T, Potey C, Leclercq C, Viard R, Kuchcinski G, Lopes R, Pruvo JP, Pigny P, Garçon G, Simonin O, Carpentier J, Rolland AS, Nyholm D, Scherfler C, Mangin JF, Chupin M, Bordet R, Dexter DT, Fradette C, Spino M, Tricta F, Ayton S, Bush AI, Devedjian JC, Duce JA, Cabantchik I, Defebvre L, Deplanque D, Moreau C. Trial of Deferiprone in Parkinson's Disease. N Engl J Med. 2022;387(22):2045-55.##Cheskes S, Verbeek PR, Drennan IR, McLeod SL, Turner L, Pinto R, Feldman M, Davis M, Vaillancourt C, Morrison LJ, Dorian P, Scales DC. Defibrillation Strategies for Refractory Ventricular Fibrillation. N Engl J Med. 2022;387(21):1947-56.##Andersen-Ranberg NC, Poulsen LM, Perner A, Wetterslev J, Estrup S, Hästbacka J, Morgan M, Citerio G, Caballero J, Lange T, Kjær MN, Ebdrup BH, Engstrøm J, Olsen MH, Oxenbøll Collet M, Mortensen CB, Weber SO, Andreasen AS, Bestle MH, Uslu B, Scharling Pedersen H, Gramstrup Nielsen L, Toft Boesen HC, Jensen JV, Nebrich L, La Cour K, Laigaard J, Haurum C, Olesen MW, Overgaard-Steensen C, Westergaard B, Brand B, Kingo Vesterlund G, Thornberg Kyhnauv P, Mikkelsen VS, Hyttel-Sørensen S, de Haas I, Aagaard SR, Nielsen LO, Eriksen AS, Rasmussen BS, Brix H, Hildebrandt T, Schønemann-Lund M, Fjeldsøe-Nielsen H, Kuivalainen AM, Mathiesen O. Haloperidol for the Treatment of Delirium in ICU Patients. N Engl J Med. 2022;387(26):2425-35.##Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. J Med Syst. 2023;47(1):33.##Manohar N, Prasad SS. Use of ChatGPT in Academic Publishing: A Rare Case of Seronegative Systemic Lupus Erythematosus in a Patient With HIV Infection. Cureus. 2023;15(2):e34616.##Nature. Tools such as ChatGPT threaten transparent science; here are our ground rules for their use 2023 [Available from: https://www.nature.com/articles/d41586-023-00191-1.##Elsevier. Publishing Ethics 2023 [Available from: https://www.elsevier.com/about/policies/publishing-ethics.##Derungs A. What are the best article generator software tools in the marketplace? 2023 [Available from: https://www.nichepursuits.com/best-article-generator-software/.##Stokel-Walker C. AI bot ChatGPT writes smart essays-should academics worry? Nature. 2022.##Moons P, Van Bulck L. ChatGPT: Can artificial intelligence language models be of value for cardiovascular nurses and allied health professionals. Eur J Cardiovasc Nurs. 2023.##Huh S. Are ChatGPT's knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study. J Educ Eval Health Prof. 2023;20:1.##https://doi.org/10.3352/jeehp.2023.20.1##Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, Madriaga M, Aggabao R, Diaz-Candido G, Maningo J, Tseng V. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198.##Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, Chartash D. How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med Educ. 2023;9:e45312.##Anders BA. Why ChatGPT is such a big deal for education. C2C Digital Magazine. 2023;1(18):4.##Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT - Reshaping medical education and clinical management. Pak J Med Sci. 2023;39(2):605-7.##D'Amico RS, White TG, Shah HA, Langer DJ. I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care…. Neurosurgery. 2023;92(4):663-4.##Yeo YH, Samaan JS, Ng WH, Ting PS, Trivedi H, Vipani A, Ayoub W, Yang JD, Liran O, Spiegel B, Kuo A. Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol. 2023.##Mack JW, Block SD, Nilsson M, Wright A, Trice E, Friedlander R, Paulk E, Prigerson HG. Measuring therapeutic alliance between oncologists and patients with advanced cancer: the Human Connection Scale. Cancer. 2009;115(14):3302-11.##Horvath AO, Luborsky L. The role of the therapeutic alliance in psychotherapy. J Consult Clin Psychol. 1993;61(4):561-73.##Will ChatGPT transform healthcare? Nature Medicine. 2023;29(3):505-6.##Perrigo B. AI Chatbots are getting better. But an interview with ChatGPT reveals their limits: TIME; 2022 [Available from: https://time.com/6238781/chatbot-chatgpt-ai-interview/.##Else H. Abstracts written by ChatGPT fool scientists. Nature. 2023;613(7944):423.##Blanco-Gonzalez A, Cabezon A, Seco-Gonzalez A, Conde-Torres D, Antelo-Riveiro P, Pineiro A, Garcia-Fandino R. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. arXiv preprint arXiv:221208104. 2022.##van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: five priorities for research. Nature. 2023;614(7947):224-6.##Marchandot B, Matsushita K, Carmona A, Trimaille A, Morel O. ChatGPT: the next frontier in academic writing for cardiologists or a pandora's box of ethical dilemmas. Eur Heart J Open. 2023;3(2):oead007.##The Lancet Digital H. ChatGPT: friend or foe? Lancet Digit Health. 2023;5(3):e102.##Cahan P, Treutlein B. A conversation with ChatGPT on the role of computational systems biology in stem cell research. Stem Cell Reports. 2023;18(1):1-2.##Stokel-Walker C, Van Noorden R. What ChatGPT and generative AI mean for science. Nature. 2023;614(7947):214-6.##Sallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare (Basel). 2023;11(6).##Kuehn BM. More than one-third of US individuals use the Internet to self-diagnose. Jama. 2013;309(8):756-7.##https://doi.org/10.1001/jama.2013.629## ##

The application of artificial intelligence (AI) in the diagnosis of platelet disorders 2 2 Artificial intelligence (AI), machine learning, and deep learning are emerging technologies with the potential to revolutionize the diagnosis and treatment of various diseases, ranging from cancer to cardiovascular disorders. These advanced algorithms have the ability to learn patterns and associations, enabling them to make predictions and enhance therapeutic approaches. Among the conditions that can benefit from AI's capabilities are platelet disorders, which at least in some cases may lead to life-threatening excessive bleeding. The conventional methods to diagnose these disorders mainly rely on manual or automated blood cell counting and morphology analysis that are prone to errors. In recent years, researchers have turned to machine learning models to predict platelet disorders, including drug-induced immune thrombocytopenia (DITP), sepsis-associated thrombocytopenia (SAT), immune thrombocytopenic purpura (ITP), disseminated intravascular coagulation (DIC), as well as thrombocytosis. These studies have yielded promising results, demonstrating satisfactory efficacy and accuracy. Our analysis revealed that key predictive parameters include the patient's medical history, platelet counts, and coagulation factors. Notably, the support vector machine (SVM) algorithm exhibited the highest performance in predicting platelet disorders, achieving the highest accuracy score while analyzing a relatively lower number of parameters. 68 83 2023/06/252023/06/242023/07/12023/07/12023/05/262023/07/2 1402/4/11 2023/08/172023/08/132023/08/12023/08/292023/07/172023/08/28 1402/6/6 Sahar Tavakkoli Shiraji Sahar Tavakkoli Shiraji Department of Internal Medicine, School of Medicine, Research Institute for Oncology, Hematology and Cell Therapy, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran Sahar.ts78@yahoo.com Mohammad-Javad Sanaei Mohammad-Javad Sanaei Cellular and Molecular Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, Iran javadsanaei137@gmail.com Artificial intelligence (AI) Machine learning Deep learning Platelet disorder Thrombocytopenia Harrison P. Platelet function analysis. Blood reviews. 2005;19(2):111-23.##Noetzli LJ, French SL, Machlus KR. New insights into the differentiation of megakaryocytes from hematopoietic progenitors. Arteriosclerosis, thrombosis, and vascular biology. 2019;39(7):1288-300.##Haley KM. Platelet Disorders. Pediatr Rev. 2020;41(5):224-35.##Lee E-J, Lee AI. Thrombocytopenia. Primary Care: Clinics in Office Practice. 2016;43(4):543-57.##Bashash D, Hosseini-Baharanchi FS, Rezaie-Tavirani M, Safa M, Akbari Dilmaghani N, Faranoush M, et al. The Prognostic Value of Thrombocytopenia in COVID-19 Patients; a Systematic Review and Meta-Analysis. Arch Acad Emerg Med. 2020;8(1):e75.##Delshad M, Safaroghli-Azar A, Pourbagheri-Sigaroodi A, Poopak B, Shokouhi S, Bashash D. Platelets in the perspective of COVID-19; pathophysiology of thrombocytopenia and its implication as prognostic and therapeutic opportunity. Int Immunopharmacol. 2021;99:107995.##Vannucchi AM, Barbui T. Thrombocytosis and thrombosis. ASH Education program book. 2007;2007(1):363-70.##Mohan G, Malayala SV, Mehta P, Balla M. A comprehensive review of congenital platelet disorders, thrombocytopenias and thrombocytopathies. Cureus. 2020;12(10).##Al-Huniti A, Kahr WH. Inherited platelet disorders: diagnosis and management. Transfusion Medicine Reviews. 2020;34(4):277-85.##Cheng Y, Chen C, Yang J, Yang H, Fu M, Zhong X, et al. Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study. Diagnostics. 2021;11(9):1614.##Elshoeibi AM, Ferih K, Elsabagh AA, Elsayed B, Elhadary M, Marashi M, et al. Applications of Artificial Intelligence in Thrombocytopenia. Diagnostics (Basel). 2023;13(6).##Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electronic Markets. 2021;31(3):685-95.##Singh M, Pujar GV, Kumar SA, Bhagyalalitha M, Akshatha HS, Abuhaija B, et al. Evolution of machine learning in tuberculosis diagnosis: a review of deep learning-based medical applications. Electronics. 2022;11(17):2634.##Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA internal medicine. 2019;179(3):293-4.##Mani V, Ghonge MM, Chaitanya NK, Pal O, Sharma M, Mohan S, et al. A new blockchain and fog computing model for blood pressure medical sensor data storage. Computers and Electrical Engineering. 2022;102:108202.##Liu P-r, Lu L, Zhang J-y, Huo T-t, Liu S-x, Ye Z-w. Application of artificial intelligence in medicine: an overview. Current Medical Science. 2021;41(6):1105-15.##Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019;8(7):2328.##Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. Curr Oncol. 2021;28(3):1581-607.##Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med. 2022;60(12):1974-83.##Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. Int J Biol Sci. 2021;17(6):1581-7.##Huang X, Wang H, She C, Feng J, Liu X, Hu X, et al. Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy. Front Endocrinol (Lausanne). 2022;13:946915.##Nomura A, Noguchi M, Kometani M, Furukawa K, Yoneda T. Artificial Intelligence in Current Diabetes Management and Prediction. Curr Diab Rep. 2021;21(12):61.##Nahar JK, Lopez-Jimenez F. Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care. Heart Fail Clin. 2022;18(2):311-23.##Tian Y, Yang J, Lan M, Zou T. Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure. Aging (Albany NY). 2020;12(24):26221-35.##Yasmin F, Shah SMI, Naeem A, Shujauddin SM, Jabeen A, Kazmi S, et al. Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. Rev Cardiovasc Med. 2021;22(4):1095-113.##Gedefaw L, Liu CF, Ip RKL, Tse HF, Yeung MHY, Yip SP, et al. Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders. Cells. 2023;12(13).##Cai S, Han IC, Scott AW. Artificial intelligence for improving sickle cell retinopathy diagnosis and management. Eye (Lond). 2021;35(10):2675-84.##El Alaoui Y, Elomri A, Qaraqe M, Padmanabhan R, Yasin Taha R, El Omri H, et al. A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects. J Med Internet Res. 2022;24(7):e36490.##Radakovich N, Cortese M, Nazha A. Acute myeloid leukemia and artificial intelligence, algorithms and new scores. Best Pract Res Clin Haematol. 2020;33(3):101192.##Belcic T, Cernelc P, Sever M. PB2225 Artificial intelligence aiding in diagnosis of JAK2 V617F negative patients with WHO defined essential thrombocythemia. HemaSphere. 2019;3(S1):998.##Greenberg EM, Kaled ES. Thrombocytopenia. Crit Care Nurs Clin North Am. 2013;25(4):427-34, v.##Smock KJ, Perkins SL. Thrombocytopenia: an update. Int J Lab Hematol. 2014;36(3):269-78.##Aster RH, Curtis BR, McFarland JG, Bougie DW. Drug-induced immune thrombocytopenia: pathogenesis, diagnosis, and management. J Thromb Haemost. 2009;7(6):911-8.##Curtis BR. Drug-induced immune thrombocytopenia: incidence, clinical features, laboratory testing, and pathogenic mechanisms. Immunohematology. 2014;30(2):55-65.##Vayne C, Guéry EA, Rollin J, Baglo T, Petermann R, Gruel Y. Pathophysiology and Diagnosis of Drug-Induced Immune Thrombocytopenia. J Clin Med. 2020;9(7).##Arnold DM, Curtis BR, Bakchoul T. Recommendations for standardization of laboratory testing for drug-induced immune thrombocytopenia: communication from the SSC of the ISTH. J Thromb Haemost. 2015;13(4):676-8.##Arnold DM, Kukaswadia S, Nazi I, Esmail A, Dewar L, Smith JW, et al. A systematic evaluation of laboratory testing for drug-induced immune thrombocytopenia. J Thromb Haemost. 2013;11(1):169-76.##Wang B, Tan X, Guo J, Xiao T, Jiao Y, Zhao J, et al. Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning. Pharmaceutics. 2022;14(5).##Hogan M, Berger JS. Heparin-induced thrombocytopenia (HIT): Review of incidence, diagnosis, and management. Vasc Med. 2020;25(2):160-73.##Dhakal B, Kreuziger LB, Rein L, Kleman A, Fraser R, Aster RH, et al. Disease burden, complication rates, and health-care costs of heparin-induced thrombocytopenia in the USA: a population-based study. Lancet Haematol. 2018;5(5):e220-e31.##Cuker A, Arepally GM, Chong BH, Cines DB, Greinacher A, Gruel Y, et al. American Society of Hematology 2018 guidelines for management of venous thromboembolism: heparin-induced thrombocytopenia. Blood Adv. 2018;2(22):3360-92.##Nilius H, Cuker A, Haug S, Nakas C, Studt JD, Tsakiris DA, et al. A machine-learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: A prospective, multicenter, observational study. EClinicalMedicine. 2023;55:101745.##Natsumoto B, Yokota K, Omata F, Furukawa K. Risk factors for linezolid-associated thrombocytopenia in adult patients. Infection. 2014;42(6):1007-12.##Tajima M, Kato Y, Matsumoto J, Hirosawa I, Suzuki M, Takashio Y, et al. Linezolid-Induced Thrombocytopenia Is Caused by Suppression of Platelet Production via Phosphorylation of Myosin Light Chain 2. Biol Pharm Bull. 2016;39(11):1846-51.##Takahashi S, Tsuji Y, Kasai H, Ogami C, Kawasuji H, Yamamoto Y, et al. Classification Tree Analysis Based On Machine Learning for Predicting Linezolid-Induced Thrombocytopenia. J Pharm Sci. 2021;110(5):2295-300.##Maray I, Rodríguez-Ferreras A, Álvarez-Asteinza C, Alaguero-Calero M, Valledor P, Fernández J. Linezolid induced thrombocytopenia in critically ill patients: Risk factors and development of a machine learning-based prediction model. J Infect Chemother. 2022;28(9):1249-54.##Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama. 2016;315(8):801-10.##Vanderschueren S, De Weerdt A, Malbrain M, Vankersschaever D, Frans E, Wilmer A, et al. Thrombocytopenia and prognosis in intensive care. Crit Care Med. 2000;28(6):1871-6.##Larkin CM, Santos-Martinez M-J, Ryan T, Radomski MW. Sepsis-associated thrombocytopenia. Thrombosis research. 2016;141:11-6.##Jiang X, Wang Y, Pan Y, Zhang W. Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm. Front Med (Lausanne). 2022;9:837382.##Ling J, Liao T, Wu Y, Wang Z, Jin H, Lu F, et al. Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning. J Clin Lab Anal. 2021;35(12):e24053.##Liu S, Wang P, Shen PP, Zhou JH. Predictive Values of Red Blood Cell Distribution Width in Assessing Severity of Chronic Heart Failure. Med Sci Monit. 2016;22:2119-25.##Ellingsen TS, Lappegård J, Skjelbakken T, Braekkan SK, Hansen JB. Impact of red cell distribution width on future risk of cancer and all-cause mortality among cancer patients - the Tromsø Study. Haematologica. 2015;100(10):e387-9.##Wang AY, Ma HP, Kao WF, Tsai SH, Chang CK. Red blood cell distribution width is associated with mortality in elderly patients with sepsis. Am J Emerg Med. 2018;36(6):949-53.##Jandial A, Kumar S, Bhalla A, Sharma N, Varma N, Varma S. Elevated Red Cell Distribution Width as a Prognostic Marker in Severe Sepsis: A Prospective Observational Study. Indian J Crit Care Med. 2017;21(9):552-62.##Zainal A, Salama A, Alweis R. Immune thrombocytopenic purpura. J Community Hosp Intern Med Perspect. 2019;9(1):59-61.##Swinkels M, Rijkers M, Voorberg J, Vidarsson G, Leebeek FWG, Jansen AJG. Emerging Concepts in Immune Thrombocytopenia. Front Immunol. 2018;9:880.##Kühne T, Berchtold W, Michaels LA, Wu R, Donato H, Espina B, et al. Newly diagnosed immune thrombocytopenia in children and adults: a comparative prospective observational registry of the Intercontinental Cooperative Immune Thrombocytopenia Study Group. Haematologica. 2011;96(12):1831.##Shahgholi E, Vosough P, Sotoudeh K, Arjomandi K, Ansari S, Salehi S, et al. Intravenous immune globulin versus intravenous anti-D immune globulin for the treatment of acute immune thrombocytopenic purpura. The Indian Journal of Pediatrics. 2008;75:1231-5.##Eshghi P, Abolghasemi H, Akhlaghi AA, Ashrafi F, Bordbar M, Hajifathali A, et al. Patient and Physician Perspectives in the Management of Immune Thrombocytopenia in Iran: Responses from the ITP World Impact Survey (I-WISh). Clinical and Applied Thrombosis/Hemostasis. 2023;29:10760296221130335.##Rahiminejad MS, Sadeghi MM, Mohammadinejad P, Sadeghi B, Abolhassani H, Firoozabadi MMD, et al. Evaluation of humoral immune function in patients with chronic idiopathic thrombocytopenic purpura. Iranian Journal of Allergy, Asthma and Immunology. 2013:50-6.##Kim TO, MacMath D, Pettit RW, Kirk SE, Grimes AB, Gilbert MM, et al. Predicting Chronic Immune Thrombocytopenia in Pediatric Patients at Disease Presentation: Leveraging Clinical and Laboratory Characteristics Via Machine Learning Models. Blood. 2021;138:1023.##An Z-Y, Wu Y-J, Huang R-B, Zhou H, Huang Q-S, Fu H-X, et al. P1655: PERSONALIZED MACHINE-LEARNING-BASED PREDICTION FOR CRITICAL IMMUNE THROMBOCYTOPENIA BLEEDS: A NATIONWIDE DATA STUDY. HemaSphere. 2022;6(Suppl).##Zhang X-H, Huang R-B, Zhang J-N, Huang Q-S, Fu H-X. P1652: MACHINE-LEARNING-BASED MORTALITY PREDICTION OF ICH IN ADULTS WITH ITP: A NATIONWIDE REPRESENTATIVE MULTICENTRE STUDY. HemaSphere. 2022;6(Suppl).##Gando S, Levi M, Toh CH. Disseminated intravascular coagulation. Nat Rev Dis Primers. 2016;2:16037.##Taylor FB, Jr., Toh CH, Hoots WK, Wada H, Levi M. Towards definition, clinical and laboratory criteria, and a scoring system for disseminated intravascular coagulation. Thromb Haemost. 2001;86(5):1327-30.##Gando S, Iba T, Eguchi Y, Ohtomo Y, Okamoto K, Koseki K, et al. A multicenter, prospective validation of disseminated intravascular coagulation diagnostic criteria for critically ill patients: comparing current criteria. Crit Care Med. 2006;34(3):625-31.##Levi M. Diagnosis and treatment of disseminated intravascular coagulation. Int J Lab Hematol. 2014;36(3):228-36.##Yoon JG, Heo J, Kim M, Park YJ, Choi MH, Song J, et al. Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems. PLoS One. 2018;13(5):e0195861.##Yang H, Li J, Liu S, Zhang M, Liu J. An interpretable DIC risk prediction model based on convolutional neural networks with time series data. BMC Bioinformatics. 2022;23(1):471.##An Z-Y, Wu Y-J, He Y, Zhu X-L, Su Y, Wang C-C, et al. Machine-Learning Based Early Warning System for Prediction for Disseminated Intravascular Coagulation after Allogeneic Hematopoietic Stem Cell Transplantation: A Nationwide Multicenter Study. Blood. 2021;138(Supplement 1):2113-.##Schafer AI. Thrombocytosis. New England Journal of Medicine. 2004;350(12):1211-9.##Stockklausner C, Duffert CM, Cario H, Knöfler R, Streif W, Kulozik AE. Thrombocytosis in children and adolescents-classification, diagnostic approach, and clinical management. Ann Hematol. 2021;100(7):1647-65.##Mahan CE, Holdsworth MT, Welch SM, Borrego M, Spyropoulos AC. Deep-vein thrombosis: a United States cost model for a preventable and costly adverse event. Thromb Haemost. 2011;106(3):405-15.##Wells PS, Anderson DR, Rodger M, Forgie M, Kearon C, Dreyer J, et al. Evaluation of D-dimer in the diagnosis of suspected deep-vein thrombosis. N Engl J Med. 2003;349(13):1227-35.##Spyropoulos AC, Anderson FA, Jr., FitzGerald G, Decousus H, Pini M, Chong BH, et al. Predictive and associative models to identify hospitalized medical patients at risk for VTE. Chest. 2011;140(3):706-14.##Greene MT, Spyropoulos AC, Chopra V, Grant PJ, Kaatz S, Bernstein SJ, et al. Validation of Risk Assessment Models of Venous Thromboembolism in Hospitalized Medical Patients. Am J Med. 2016;129(9):1001.e9-.e18.##Ryan L, Mataraso S, Siefkas A, Pellegrini E, Barnes G, Green-Saxena A, et al. A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients. Clin Appl Thromb Hemost. 2021;27:1076029621991185.##Chen J, Dong H, Fu R, Liu X, Xue F, Liu W, et al. Machine learning analyses constructed a novel model to predict recurrent thrombosis in adults with essential thrombocythemia. Journal of Thrombosis and Thrombolysis. 2023:1-10.## ##

The application of artificial intelligence in the diagnosis and management of anemia 2 2 Anemia stands out as the most prevalent blood disorder globally. Various factors can contribute to its development, including insufficient production of red blood cells (RBCs) as well as degradation of RBCs. The functional impairment of RBCs in anemia can result in a wide spectrum of symptoms, ranging from fatigue and weakness to severe, life-threatening conditions. The primary method for detecting anemia is the commonly employed complete blood count (CBC) test. However, for certain cases and to distinguish between different types of anemia, more advanced tests become necessary. Artificial intelligence (AI) has emerged as a technology designed to replicate human intelligence and perform tasks that typically require human cognitive abilities. AI models possess the capability to comprehend patterns and associations, enabling them to recognize and analyze images. In the context of anemia, studies have demonstrated that AI algorithms can analyze images of various physical characteristics such as conjunctiva, palm, tongue, and fingernails. By estimating hemoglobin concentration, these algorithms can predict the presence of anemia. Furthermore, AI systems have also exhibited the ability to analyze clinical data, including laboratory tests and blood smears, to predict anemia and identify specific types. It is worth noting that previous studies in the context if AI applications in anemia have been conducted on relatively small populations. However, the accuracy achieved in these investigations has been satisfactory, suggesting that AI systems could potentially play a significant role in the future of anemia diagnosis and management.   84 92 2023/06/252023/06/242023/07/12023/07/12023/05/262023/07/22023/06/11 1402/3/21 2023/08/172023/08/132023/08/12023/08/292023/07/172023/08/282023/08/28 1402/6/6 Sabahat Haghi Sabahat Haghi Department of Pediatrics, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran sabahaghi@yahoo.com Reza Arjmand Reza Arjmand Department of Pediatrics, Imam Ali Hospital, Alborz University of Medical Sciences, Karaj, Iran r.arjmand30@yahoo.com Omid Safari Omid Safari Department of Pediatrics, Imam Ali Hospital, Alborz University of Medical Sciences, Karaj, Iran omidsafari50@gmail.om Anemia Artificial intelligence (AI) Algorithm Hemoglobin CBC RBC Amin J, Sharif M, Anjum MA, Yasmin M, Khattak KI, Kadry S, Seo S. An integrated design based on dual thresholding and features optimization for white blood cells detection. IEEE Access. 2021;9:151421-33.##Chi Z, Beile L, Deyu L, Yubo F. Application of multiscale coupling models in the numerical study of circulation system. Medicine in Novel Technology and Devices. 2022;14:100117.##van Galen KPM, d'Oiron R, James P, Abdul-Kadir R, Kouides PA, Kulkarni R, Mahlangu JN, Othman M, Peyvandi F, Rotellini D, Winikoff R, Sidonio RF. A new hemophilia carrier nomenclature to define hemophilia in women and girls: Communication from the SSC of the ISTH. J Thromb Haemost. 2021;19(8):1883-7.##Pujhari S, Paul S, Ahluwalia J, Rasgon JL. Clotting disorder in severe acute respiratory syndrome coronavirus 2. Rev Med Virol. 2021;31(3):e2177.##Labudde EJ, DeGroote NP, Smith S, Ebelhar J, Allen KE, Castellino SM, Wasilewski-Masker K, Brock KE. Evaluating palliative opportunities in pediatric patients with leukemia and lymphoma. Cancer Med. 2021;10(8):2714-22.##Dese K, Raj H, Ayana G, Yemane T, Adissu W, Krishnamoorthy J, Kwa T. Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images. Clin Lymphoma Myeloma Leuk. 2021;21(11):e903-e14.##Pu J, Liu Y, Zhang H, Tian L, Gui S, Yu Y, Chen X, Chen Y, Yang L, Ran Y. An integrated meta-analysis of peripheral blood metabolites and biological functions in major depressive disorder. Molecular psychiatry. 2021;26(8):4265-76.##Stevens GA, Paciorek CJ, Flores-Urrutia MC, Borghi E, Namaste S, Wirth JP, Suchdev PS, Ezzati M, Rohner F, Flaxman SR, Rogers LM. National, regional, and global estimates of anaemia by severity in women and children for 2000–19: a pooled analysis of population-representative data. The Lancet Global Health. 2022;10(5):e627-e39.##Tvedten H. Classification and laboratory evaluation of anemia. Schalm's veterinary hematology. 2022:198-208.##DeZern AE, Churpek JE. Approach to the diagnosis of aplastic anemia. Blood Adv. 2021;5(12):2660-71.##Vieth JT, Lane DR. Anemia. Hematol Oncol Clin North Am. 2017;31(6):1045-60.##Organization WH. WHO Global Anaemia estimates, 2021 Edition 2021 [Available from: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children.##Karami M, Chaleshgar M, Salari N, Akbari H, Mohammadi M. Global Prevalence of Anemia in Pregnant Women: A Comprehensive Systematic Review and Meta-Analysis. Matern Child Health J. 2022;26(7):1473-87.##Short MW, Domagalski JE. Iron deficiency anemia: evaluation and management. Am Fam Physician. 2013;87(2):98-104.##Hess SY, Owais A, Jefferds MED, Young MF, Cahill A, Rogers LM. Accelerating action to reduce anemia: Review of causes and risk factors and related data needs. Annals of the New York Academy of Sciences. 2023;1523(1):11-23.##Child WHODo, Health A. Handbook IMCI: integrated management of childhood illness: World Health Organization; 2005.##Schrier SL, Mentzer WC, Landaw SA. Approach to the adult patient with anemia. UpToDate. 2016;2016:1-33.##Saputra DCE, Sunat K, Ratnaningsih T. A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia. Healthcare (Basel). 2023;11(5).##Mitani A, Huang A, Venugopalan S, Corrado GS, Peng L, Webster DR, Hammel N, Liu Y, Varadarajan AV. Detection of anaemia from retinal fundus images via deep learning. Nat Biomed Eng. 2020;4(1):18-27.##Kasiviswanathan S, Bai Vijayan T, Simone L, Dimauro G. Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications. Electronics [Internet]. 2020; 9(8).##McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine. 2006;27(4):12-.##LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.##Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Frontiers in medicine. 2020;7:27.##Ferih K, Elsayed B, Elshoeibi AM, Elsabagh AA, Elhadary M, Soliman A, Abdalgayoom M, Yassin M. Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review. Diagnostics (Basel). 2023;13(9).##Zopfs D, Rinneburger M, Pinto Dos Santos D, Reimer RP, Laukamp KR, Maintz D, Lennartz S, Große Hokamp N. Evaluating anemia using contrast-enhanced spectral detector CT of the chest in a large cohort of 522 patients. Eur Radiol. 2021;31(6):4350-7.##Islam MM, Rahman MJ, Roy DC, Islam MM, Tawabunnahar M, Ahmed N, Maniruzzaman M. Risk factors identification and prediction of anemia among women in Bangladesh using machine learning techniques. Current Women's Health Reviews. 2022;18(1):118-33.##Mazzu-Nascimento T, Evangelista DN, Abubakar O, Sousa AS, de Souza LC, Chachá SGF, Luporini RL, Domingues LV, Silva DF, Nogueira-de-Almeida CA. Smartphone-based photo analysis for the evaluation of anemia, jaundice and COVID-19. International Journal of Nutrology. 2021;14(2):55-60.##Dithy M, Krishnapriya V. Anemia selection in pregnant women by using random prediction (Rp) classification algorithm. Int J Recent Technol Eng. 2019;8(2):2623-30.##Mannino RG, Myers DR, Tyburski EA, Caruso C, Boudreaux J, Leong T, Clifford GD, Lam WA. Smartphone app for non-invasive detection of anemia using only patient-sourced photos. Nat Commun. 2018;9(1):4924.##Berry MW, Mohamed A, Yap BW. Supervised and unsupervised learning for data science: Springer; 2019.##Asare JW, Appiahene P, Donkoh ET, Dimauro G. Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images. Engineering Reports. 2023:e12667.##Peksi NJ, Yuwono B, Florestiyanto MY. Classification of anemia with digital images of nails and palms using the Naive Bayes Method. Telematika: Jurnal Informatika dan Teknologi Informasi. 2021;18(1):118-30.##Appiahene P, Asare JW, Donkoh ET, Dimauro G, Maglietta R. Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms. BioData Min. 2023;16(1):2.##Tamir A, Jahan CS, Saif MS, Zaman SU, Islam MM, Khan AI, Fattah SA, Shahnaz C, editors. Detection of anemia from image of the anterior conjunctiva of the eye by image processing and thresholding. 2017 IEEE region 10 humanitarian technology conference (R10-HTC); 2017: IEEE.##Chen Y, Zhong K, Zhu Y, Sun Q. Two-stage hemoglobin prediction based on prior causality. Front Public Health. 2022;10:1079389.##Zhang A, Lou J, Pan Z, Luo J, Zhang X, Zhang H, Li J, Wang L, Cui X, Ji B, Chen L. Prediction of anemia using facial images and deep learning technology in the emergency department. Front Public Health. 2022;10:964385.##Ghosh A, Mukherjee J, Chakravorty N. A Low-Cost Test for Anemia Using an Artificial Neural Network. Comput Methods Programs Biomed. 2023;229:107251.##Dimauro G, Camporeale MG, Dipalma A, Guarini A, Maglietta R. Anaemia detection based on sclera and blood vessel colour estimation. Biomedical Signal Processing and Control. 2023;81:104489.##Hortinela CC, Balbin JR, Fausto JC, Divina PDC, Felices JPT, editors. Identification of abnormal red blood cells and diagnosing specific types of anemia using image processing and support vector machine. 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM); 2019: IEEE.##Shahzad M, Umar AI, Shirazi SH, Khan Z, Khan A, Assam M, Mohamed A, Attia E-A. Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3-Tier Deep Neural Network. Applied Sciences. 2022;12(10):5030.##El-kenawy E, SM E. A Machine Learning Model for Hemoglobin Estimation and Anemia Classification. International Journal of Computer Science and Information Security (IJCSIS). 2019;17(2):100-8.##Jaiswal M, Srivastava A, Siddiqui TJ, editors. Machine learning algorithms for anemia disease prediction. Recent Trends in Communication, Computing, and Electronics: Select Proceedings of IC3E 2018; 2019: Springer.##Wang M, Dong C, Gao Y, Li J, Han M, Wang L. A deep learning model for the automatic recognition of aplastic anemia, myelodysplastic syndromes, and acute myeloid leukemia based on bone marrow smear. Frontiers in Oncology. 2022;12:844978.##Çil B, Ayyıldız H, Tuncer T. Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system. Medical hypotheses. 2020;138:109611.##Kabootarizadeh L, Jamshidnezhad A, Koohmareh Z. Differential diagnosis of iron-deficiency anemia from β-thalassemia trait using an intelligent model in comparison with discriminant indexes. Acta Informatica Medica. 2019;27(2):78.##Khan JR, Chowdhury S, Islam H, Raheem E. Machine learning algorithms to predict the childhood anemia in Bangladesh. Journal of Data Science. 2019;17(1):195-218.##Aliyu HA, Razak MAA, Sudirman R, Ramli N. A deep learning AlexNet model for classification of red blood cells in sickle cell anemia. Int J Artif Intell. 2020;9(2):221-8.##Kühl N, Schemmer M, Goutier M, Satzger G. Artificial intelligence and machine learning. Electronic Markets. 2022;32(4):2235-44.##Staszak M, Staszak K, Wieszczycka K, Bajek A, Roszkowski K, Tylkowski B. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure-biological activity relationship. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2022;12(2):e1568.##Lankhorst CE, Wish JB. Anemia in renal disease: Diagnosis and management. Blood Reviews. 2010;24(1):39-47.##Ly J, Marticorena R, Donnelly S. Red blood cell survival in chronic renal failure. American Journal of Kidney Diseases. 2004;44(4):715-9.##Locatelli F, Nissenson AR, Barrett BJ, Walker RG, Wheeler DC, Eckardt KU, Lameire NH, Eknoyan G. Clinical practice guidelines for anemia in chronic kidney disease: problems and solutions. A position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney International. 2008;74(10):1237-40.##Kalicki RM, Uehlinger DE. Red cell survival in relation to changes in the hematocrit: more important than you think. Blood purification. 2008;26(4):355-60.##Martínez-Martínez JM, Escandell-Montero P, Barbieri C, Soria-Olivas E, Mari F, Martínez-Sober M, Amato C, Serrano López AJ, Bassi M, Magdalena-Benedito R, Stopper A, Martín-Guerrero JD, Gatti E. Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques. Computer Methods and Programs in Biomedicine. 2014;117(2):208-17.##Escandell-Montero P, Chermisi M, Martínez-Martínez JM, Gómez-Sanchis J, Barbieri C, Soria-Olivas E, Mari F, Vila-Francés J, Stopper A, Gatti E, Martín-Guerrero JD. Optimization of anemia treatment in hemodialysis patients via reinforcement learning. Artificial Intelligence in Medicine. 2014;62(1):47-60.##Barbieri C, Mari F, Stopper A, Gatti E, Escandell-Montero P, Martínez-Martínez JM, Martín-Guerrero JD. A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Computers in Biology and Medicine. 2015;61:56-61.##Brier ME, Gaweda AE. Artificial intelligence for optimal anemia management in end-stage renal disease. Kidney International. 2016;90(2):259-61.## ##

Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics 2 2 Background: To guarantees patient survival and reduce the consumption of pharmaceutical and medical resources, an accurate diagnosis and assessment of COVID-19 severity are crucial. Since the outbreak of the pandemic, researchers have evaluated, identified, and predicted the severity of COVID-19 using a range of AI techniques. Due to the lack of a systematic review in the field of analysis of these studies, the present research rigorously reviewed all the pertinent literature. Methods: Between December 1, 2019, and January 1, 2022, 762 articles were found by searching the PubMed, Scopus, Web of Science, and Scholar databases using the search method. 34 papers were chosen from this group as the research community's representatives using inclusion and exclusion criteria. Results: By looking at the machine learning approach used in this research, it can be seen that XGBoost and SVM algorithms were more prevalent and effective in identifying the severity of the condition, according to the results of the data analysis used in this study. A set of impressive features, including clinical, demographic, laboratory, and serology data, was used to calculate the severity of COVID-19 using ML algorithms. By calculating the performance metric, it can be concluded that the ML methods had high sensitivity and specificity in determining the severity of COVID-19. Conclusion: Deep learning methods, as cutting-edge methods, have a significant tangible capacity for providing an accurate and efficient intelligent system for detecting and estimating the severity of COVID-19. It is recommended that in the future or other variants of COVID-19 epidemics, AI-based systems in conjunction with IoT, cloud storage, and 5G technologies be used to remove geographical problems in the rapid estimation of disease severity, immediate epidemic control before the pandemic, epidemic management, and lower treatment costs 93 111 2023/06/252023/06/242023/07/12023/07/12023/05/262023/07/22023/06/112023/06/12 1402/3/22 2023/08/172023/08/132023/08/12023/08/292023/07/172023/08/282023/08/282023/08/12 1402/5/21 Mustafa Ghaderzadeh Mustafa Ghaderzadeh Department of Artificial Intelligence, Smart University of Medical Science, Tehran, Iran Mustaf.ghaderzadeh@sbmu.ac.ir Farkhondh Asadi Farkhondh Asadi Health Information Management, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Asadifar@sbmu.ac.ir Nahid Ramezan Ghorbani Nahid Ramezan Ghorbani Department of Development & Coordination Scientific Information and Publications, Deputy of Research & Technology, Ministry of Health & Medical Education, Tehran, Iran. ghorbani@research.ac.ir Sohrab Almasi Sohrab Almasi Health Information Management, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Almasi.sohrab@sbmu.ac.ir Tania Taami Tania Taami Department of Computer Science, Tallahassee, FL, USA Tania Taami <tania.taami@ieee.org> Artificial Intelligence Pandemic COVID-19 XGBoost SVM IoT 5G Chang L, Yan Y, Wang L. Coronavirus disease 2019: coronaviruses and blood safety. Transfus Med Rev. 2020;34(2):75-80.##Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-20.##Here's what we know about the new Omicron variant XBB.1.5 | Gavi, the Vaccine Alliance [Internet]. [cited 2023 Jan 14]. Available from: https://www.gavi.org/vaccineswork/heres-what-we-know-about-new-omicron-variant-xbb15##Govindan K, Mina H, Alavi B. A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transp Res Part E Logist Transp Rev. 2020;138:101967.##Liu Y, Wang Z, Ren J, Tian Y, Zhou M, Zhou T, et al. A COVID-19 risk assessment decision support system for general practitioners: design and development study. J Med Internet Res. 2020;22(6):e19786.##Ghaderzadeh M, Asadi F. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. Maietta S, editor. J Healthc Eng [Internet]. 2021;2021:6677314. Available from:##Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, et al. Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment. IEEE Internet Things J [Internet]. 2021;8(21):15919-28. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099572141;doi=10.1109%2FJIOT.2021.3050775;partnerID=40;md5=da9f1a8be5020223ff815379e01f73e2##Akram T, Attique M, Gul S, Shahzad A, Altaf M, Naqvi S, et al. A novel framework for rapid diagnosis of COVID-19 on computed tomography scans. Pattern Anal Appl. 2021;24(3):951-64.##https://doi.org/10.1007/s10044-021-00969-x##Banerjee A, Ray S, Vorselaars B, Kitson J, Mamalakis M, Weeks S, et al. Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. Int Immunopharmacol. 2020;86:106705.##Ghaderzadeh M, Aria M, Asadi F. X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic. Fancellu A, editor. Biomed Res Int [Internet]. 2021;2021:9942873. Available from:##Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M. Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study. J Med Internet Res. 2021;23(4):e27468.##Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology. 2020;##Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol. 2020;30(6):3306-9.##Ghaderzadeh M, Eshraghi MA, Asadi F, Hosseini A, Jafari R, Bashash D, et al. Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models. Corsi C, editor. Comput Math Methods Med [Internet]. 2022;2022:4838009. Available from:##Rahim A, Maqbool A, Mirza A, Afzal F, Asghar I. DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions. Electron [Internet]. 2022;11(3). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124364980;doi=10.3390%2Felectronics11030458;partnerID=40;md5=0128860674717312f1d9f4d81dc21f9b##Eshraghi MA, Ayatollahi A, Shokouhi SB. COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images. BMC Med Imaging [Internet]. 2023;23(1):83. Available from:##Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32.##Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-73.##Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, Gefen-Halevi S, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435-43.##Carvalho ARS, Guimarães A, Werberich GM, de Castro SN, Pinto JSF, Schmitt WR, et al. COVID-19 chest computed tomography to stratify severity and disease extension by artificial neural network computer-aided diagnosis. Front Med. 2020;7:577609.##Gull H, Krishna G, Aldossary MI, Iqbal SZ. Severity prediction of COVID-19 patients using machine learning classification algorithms: A case study of small city in Pakistan with minimal health facility. In: 2020 IEEE 6th international conference on computer and communications (ICCC). IEEE; 2020. p. 1537-41.##Chen X, Liu Z. Early prediction of mortality risk among severe COVID-19 patients using machine learning. 2020;##Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: An international multicentre study. Eur Respir J [Internet]. 2020;56(2). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089787826;doi=10.1183%2F13993003.01104-2020;partnerID=40;md5=8a919fecbe59c412edd2c11dc2c4bb41##Yan L, Zhang H-T, Xiao Y, Wang M, Sun C, Liang J, et al. Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. MedRxiv. 2020;27:2020.##Sun L, Song F, Shi N, Liu F, Li S, Li P, et al. Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19. J Clin Virol [Internet]. 2020;128:104431. Available from: https://www.sciencedirect.com/science/article/pii/S1386653220301736##Aswathy AL, Hareendran A, SS VC. COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network. J Infect Public Health. 2021;14(10):1435-45.##Ahmed F, Hossain MS, Islam RU, Andersson K. An evolutionary belief rule-based clinical decision support system to predict covid-19 severity under uncertainty. Appl Sci. 2021;11(13):5810.##Dastider AG, Sadik F, Fattah SA. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Comput Biol Med. 2021;132:104296.##Domínguez-Olmedo JL, Gragera-Martínez Á, Mata J, Álvarez VP. Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation. J Med Internet Res. 2021;23(4):e26211.##Guan X, Zhang B, Fu M, Li M, Yuan X, Zhu Y, et al. Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med. 2021;53(1):257-66.##Ho TT, Park J, Kim T, Park B, Lee J, Kim JY, et al. Deep learning models for predicting severe progression in COVID-19-infected patients: Retrospective study. JMIR Med informatics. 2021;9(1):e24973.##Ibrahim MR, Youssef SM, Fathalla KM. Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment. J Ambient Intell Humaniz Comput. 2021;1-24.##Jayaraj T, Samath JA. Disease forecasting and severity prediction model for COVID-19 using correlated feature extraction and feed-forward artificial neural networks. Int J Eng Trends Technol [Internet]. 2021;69(8):126-37. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112439628;doi=10.14445%2F22315381%2FIJETT-V69I8P216;partnerID=40;md5=08f42691f547d60c529ad4cc8735c032##Kang J, Chen T, Luo H, Luo Y, Du G, Jiming-Yang M. Machine learning predictive model for severe COVID-19. Infect Genet Evol. 2021;90:104737.##Kivrak M, Guldogan E, Colak C. Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods. Comput Methods Programs Biomed [Internet]. 2021;201:105951. Available from: https://www.sciencedirect.com/science/article/pii/S0169260721000250##La Salvia M, Secco G, Torti E, Florimbi G, Guido L, Lago P, et al. Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification. Comput Biol Med. 2021;136:104742.##Li Z, Zhao W, Shi F, Qi L, Xie X, Wei Y, et al. A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning. Med Image Anal. 2021;69:101978.##Aktar S, Ahamad MM, Rashed-Al-Mahfuz M, Azad AKM, Uddin S, Kamal AHM, et al. Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med informatics. 2021;9(4):e25884.##Qiblawey Y, Tahir A, Chowdhury MEH, Khandakar A, Kiranyaz S, Rahman T, et al. Detection and severity classification of COVID-19 in CT images using deep learning. Diagnostics. 2021;11(5):893.##Quiroz JC, Feng Y-Z, Cheng Z-Y, Rezazadegan D, Chen P-K, Lin Q-T, et al. Development and validation of a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data: retrospective study. JMIR Med Informatics. 2021;9(2):e24572.##Sayed SA-F, Elkorany AM, Mohammad SS. Applying different machine learning techniques for prediction of COVID-19 severity. Ieee Access. 2021;9:135697-707.##Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Ma T, et al. Severity assessment of COVID-19 using CT image features and laboratory indices. Phys Med Biol. 2021;66(3):35015.##Wong KC-Y, Xiang Y, Yin L, So H-C. Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. JMIR public Heal Surveill. 2021;7(9):e29544.##Aljameel SS, Khan IU, Aslam N, Aljabri M, Alsulmi ES. Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients. Sci Program [Internet]. 2021;2021. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105331575;doi=10.1155%2F2021%2F5587188;partnerID=40;md5=ae8e1be4c2eb66f008c5d3704c7800a0##Alotaibi A, Shiblee M, Alshahrani A. Prediction of severity of covid-19-infected patients using machine learning techniques. Computers [Internet]. 2021;10(3). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103054944;doi=10.3390%2Fcomputers10030031;partnerID=40;md5=7e3958031aaf07555070591dd8206bb2##Amini N, Shalbaf A. Automatic classification of severity of COVID‐19 patients using texture feature and random forest based on computed tomography images. Int J Imaging Syst Technol. 2022;32(1):102-10.##Blagojević A, Šušteršič T, Lorencin I, Šegota SB, Anđelić N, Milovanović D, et al. Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression. Comput Biol Med [Internet]. 2021;138. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115011860;doi=10.1016%2Fj.compbiomed.2021.104869;partnerID=40;md5=792238ba166be6e192e1631b15ad0272##Chen Y, Ouyang L, Bao FS, Li Q, Han L, Zhang H, et al. A multimodality machine learning approach to differentiate severe and nonsevere COVID-19: model development and validation. J Med Internet Res. 2021;23(4):e23948.##Chung H, Ko H, Kang WS, Kim KW, Lee H, Park C, et al. Prediction and feature importance analysis for severity of COVID-19 in South Korea using artificial intelligence: model development and validation. J Med Internet Res. 2021;23(4):e27060.##de Fátima Cobre A, Stremel DP, Noleto GR, Fachi MM, Surek M, Wiens A, et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput Biol Med. 2021;134:104531.##Blagojević A, Šušteršič T, Lorencin I, Šegota SB, Anđelić N, Milovanović D, et al. Artificial intelligence approach towards assessment of condition of COVID-19 patients-Identification of predictive biomarkers associated with severity of clinical condition and disease progression. Comput Biol Med. 2021 Nov;138:104869.##Garavand A, Behmanesh A, Aslani N, Sadeghsalehi H, Ghaderzadeh M. Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art. El Kafhali S, editor. Int J Intell Syst [Internet]. 2023;2023:6442756. Available from:##Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. Vol. 11, Journal of applied biomedicine. Elsevier; 2013. p. 47-58.##Hu C, Liu Z, Jiang Y, Shi O, Zhang X, Xu K, et al. Early prediction of mortality risk among patients with severe COVID-19, using machine learning. Int J Epidemiol. 2020;49(6):1918-29.##Torrealba-Rodriguez O, Conde-Gutiérrez RA, Hernández-Javier AL. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos, Solitons & Fractals. 2020;138:109946.##Haritha D, Swaroop N, Mounika M. Prediction of COVID-19 Cases Using CNN with X-rays. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS). IEEE; 2020. p. 1-6.## ##

AI-driven malaria diagnosis: developing a robust model for accurate detection and classification of malaria parasites 2 2 Background: Malaria remains a significant global health problem, with a high incidence of cases and a substantial number of deaths yearly. Early identification and accurate diagnosis play a crucial role in effective malaria treatment. However, underdiagnosis presents a significant challenge in reducing mortality rates, and traditional laboratory diagnosis methods have limitations in terms of time consumption and error susceptibility. To overcome these challenges, researchers have increasingly utilized Machine Learning techniques, specifically neural networks, which provide faster, cost-effective, and highly accurate diagnostic capabilities. Methods: This study aimed to compare the performance of a traditional neural network (NN) with a convolutional neural network (CNN) in the diagnosis and classification of different types of malaria using blood smear images. We curated a comprehensive malaria dataset comprising 1,920 images obtained from 84 patients suspected of having various malaria strains. The dataset consisted of 624 images of Falciparum, 548 images of Vivax, 588 images of Ovale, and 160 images from suspected healthy individuals, obtained from local hospitals in Iran. To ensure precise analysis, we developed a unique segmentation model that effectively eliminated therapeutically beneficial cells from the image context, enabling accurate analysis using artificial intelligence algorithms. Results: The evaluation of the traditional NN and the proposed 6-layer CNN model for image classification yielded average accuracies of 95.11% and 99.59%, respectively. These results demonstrate that the CNN, as a primary algorithm of deep neural networks (DNN), outperforms the traditional NN in analyzing different classes of malaria images. The CNN model demonstrated superior diagnostic performance, delivering enhanced accuracy and reliability in the classifying of malaria cases. Conclusion: This research underscores the potential of ML technologies, specifically CNNs, in improving malaria diagnosis and classification. By leveraging advanced image analysis techniques, including the developed segmentation model, CNN showcased remarkable proficiency in accurately identifying and classifying various malaria parasites from blood smear images. The adoption of machine learning-based approaches holds promise for more effective management and treatment of malaria, addressing the challenges of underdiagnosis and improving patient outcomes. 112 124 2023/06/252023/06/242023/07/12023/07/12023/05/262023/07/22023/06/112023/06/122023/06/9 1402/3/19 2023/08/172023/08/132023/08/12023/08/292023/07/172023/08/282023/08/282023/08/122023/08/18 1402/5/27 Zohre Fasihfar Zohre Fasihfar Faculty Member, Electrical and Computer Engineering Department, Hakim Sabzevari University, Iran fasihfar@hsu.ac.ir Hamidreza Rokhsati Hamidreza Rokhsati Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy Hamidrezarokhsati@yahoo.com Hamidreza Sadeghsalehi Hamidreza Sadeghsalehi Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran hamidreza.salehi10@gmail.com Mustafa Ghaderzadeh Mustafa Ghaderzadeh Department of Artificial Intelligence, Smart University of Medical Science, Tehran, Iran Mustaf.ghaderzadeh@sbmu.ac.ir Mehdi Gheisari Mehdi Gheisari Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences, Chennai, India mehdi.gheisari61@gmail.com Malaria Parasites Image Processing Artificial Neural Network Deep Learning Convolutional Neural Network Poostchi M, Ersoy I, McMenamin K, Gordon E, Palaniappan N, Pierce S, et al. Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy. J Med Imaging [Internet]. 2018;5(4):44506. Available from:##Díaz G, González FA, Romero E. A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. J Biomed Inform. 2009;42(2):296-307.##Poostchi M, Ersoy I, McMenamin K, Gordon E, Palaniappan N, Pierce S, et al. Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy. J Med Imaging. 2018;5(4):44506.##Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Vol. 42, Medical Image Analysis. Elsevier B.V.; 2017. p. 60-88.##Razzak MI. Automatic detection and classification of malarial parasite. Int J Biometrics Bioinforma. 2015;9(1):1-12.##Pattanaik PA, Swarnkar T, Sheet D. Object detection technique for malaria parasite in thin blood smear images. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2017. p. 2120-3.##Ross NE, Pritchard CJ, Rubin DM, Duse AG. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Biol Eng Comput. 2006;44:427-36.##Landier J, Parker DM, Thu AM, Carrara VI, Lwin KM, Bonnington CA, et al. The role of early detection and treatment in malaria elimination. Malar J [Internet]. 2016;15(1):363. Available from:##Kalkan SC, Sahingoz OK. Deep Learning Based Classification of Malaria from Slide Images. In: 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). 2019. p. 1-4.##Aqeel S, Haider Z, Khan W. Towards digital diagnosis of malaria: How far have we reached? J Microbiol Methods [Internet]. 2023;204:106630. Available from: https://www.sciencedirect.com/science/article/pii/S0167701222002251##Gourisaria MK, Das S, Sharma R, Rautaray SS, Pandey M. A deep learning model for malaria disease detection and analysis using deep convolutional neural networks. Int J Emerg Technol. 2020;11(2):699-704.##Mustafa WA, Alquran H, Aihsan MZ, Saifizi M, Khairunizam W, Abdul-Nasir AS, et al. Malaria parasite diagnosis using computational techniques: a comprehensive review. In: Journal of Physics: Conference Series. IOP Publishing; 2021. p. 12031.##Jdey I, Hcini G, Ltifi H. Deep learning and machine learning for Malaria detection: overview, challenges and future directions. arXiv Prepr arXiv220913292. 2022;##ghi M, Asadi F, Hosseini A, Bashash D, Abolghasemi H, Roshanpour A. Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review. Wang P, editor. Sci Program. 2021;2021:9933481.##Sadoughi F, Ghaderzadeh M. A hybrid particle swarm and neural network approach for detection of prostate cancer from benign hyperplasia of prostate. In: e-Health-For Continuity of Care. IOS Press; 2014. p. 481-5.##Garavand A, Behmanesh A, Aslani N, Sadeghsalehi H, Ghaderzadeh M. Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art. El Kafhali S, editor. Int J Intell Syst [Internet]. 2023;2023:6442756. Available from:##Hosseini A, Eshraghi MA, Taami T, Sadeghsalehi H, Hoseinzadeh Z, Ghaderzadeh M, et al. A mobile application based on efficient lightweight CNN model for classification of B-ALL cancer from non-cancerous cells: A design and implementation study. Informatics Med Unlocked [Internet]. 2023;39:101244. Available from: https://www.sciencedirect.com/science/article/pii/S2352914823000862##Gheisari M, Ebrahimzadeh F, Rahimi M, Moazzamigodarzi M, Liu Y, Dutta Pramanik PK, et al. Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey. CAAI Trans Intell Technol. 2023;##AI-Driven Malaria Diagnosis: Developing a Robust Model for Accurate Detection and Classification of Malaria Parasites.##Movellan JR. Tutorial on Gabor filters. Open source Doc. 2002;40:1-23.##Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122-31.##Loddo A, Di Ruberto C, Kocher M. Recent advances of malaria parasites detection systems based on mathematical morphology. Sensors. 2018;18(2):513.##Cantrell K, Erenas MM, de Orbe-Payá I, Capitán-Vallvey LF. Use of the hue parameter of the hue, saturation, value color space as a quantitative analytical parameter for bitonal optical sensors. Anal Chem. 2010;82(2):531-42.##Forcado MRG, Estrada JE. Model development of marble quality identification using thresholding, sobel edge detection and gabor filter in a mobile platform. In Graduate Programs, Technological Institute of the Philippines - Manila, Quiapo, Manila, Philippines; 2019. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064128503;doi=10.1109%2FHNICEM.2018.8666359;partnerID=40;md5=40eec67b571d342da5a28a6a592ea18a##Kamarainen J-K, Kyrki V, Kalviainen H. Invariance properties of Gabor filter-based features-overview and applications. IEEE Trans image Process. 2006;15(5):1088-99.##Ghaderzadeh M, Eshraghi MA, Asadi F, Hosseini A, Jafari R, Bashash D, et al. Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models. Corsi C, editor. Comput Math Methods Med [Internet]. 2022;2022:4838009. Available from:##Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M. Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study. J Med Internet Res. 2021;23(4):e27468.## ##

Application of Artificial Intelligence in Celiac Disease: from diagnosis to patient follow-up 2 2 Celiac disease (CD) is an autoimmune digestive condition that is distinguished by inflammation of the small intestine as a result of gluten ingestion. Its worldwide prevalence is approximately 1%. Despite progress in understanding CD, challenges in pathogenesis, diagnosis, treatment, and management persist. Genetic and environmental factors, such as HLA and non-HLA genes, gluten, gut microbiota imbalance, and immune responses involving CD4+ T cells, influence CD. Diagnostic challenges arise due to diverse clinical presentations and overlap with other gastrointestinal disorders. Following a gluten-free diet (GFD) strictly is the primary treatment for CD, but this diet presents social, psychological, and financial hurdles. Artificial intelligence (AI) has emerged as a potent instrument in CD management. Techniques like machine learning (ML), deep learning (DL), natural language processing (NLP), and computer-aided algorithms have shown promise in CD diagnosis by improving microbiome analysis, disease prediction, interpretation of medical records and endoscopy images. AI-based decision-support systems can aid in diagnosis. AI-driven personalized nutrition and gluten contamination monitoring techniques offer potential improvements for treatment. Overall, AI has potential in addressing CD challenges and enhancing patient outcomes.   125 137 2023/06/252023/06/242023/07/12023/07/12023/05/262023/07/22023/06/112023/06/122023/06/92023/09/23 1402/7/1 2023/08/172023/08/132023/08/12023/08/292023/07/172023/08/282023/08/282023/08/122023/08/182023/11/9 1402/8/18 Mohadeseh Mahmoudi Ghehsareh Mohadeseh Mahmoudi Ghehsareh Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran mohimahmoudi1998@gmail.com Nastaran Asri Nastaran Asri Celiac Disease and Gluten Related Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran nastaran.asri26@gmail.com Sepehr Maleki Sepehr Maleki Department of Computer Science, University of Tabriz, Tabriz, Iran sepehrmaleki88@gmail.com Mostafa Rezaei-Tavirani Mostafa Rezaei-Tavirani Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. tavirany@yahoo.com Somayeh Jahani-Sherafat Somayeh Jahani-Sherafat Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran jahani_somayeh@yahoo.com Mohammad Rostami-Nejad Mohammad Rostami-Nejad Celiac Disease and Gluten Related Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran m.rostamii@gmail.com Artificial intelligence Celiac disease Machine learning Deep learning Diagnosis Barton SH, Murray JA. Celiac disease and autoimmunity in the gut and elsewhere. Gastroenterology Clinics of North America. 2008;37(2):411-28.##Cichewicz AB, Mearns ES, Taylor A, Boulanger T, Gerber M, Leffler DA, Drahos J, Sanders DS, Thomas Craig KJ, Lebwohl B. Diagnosis and treatment patterns in celiac disease. Digestive diseases and sciences. 2019;64:2095-106.##Han A, Newell EW, Glanville J, Fernandez-Becker N, Khosla C, Chien Y-h, Davis MM. Dietary gluten triggers concomitant activation of CD4+ and CD8+ αβ T cells and γδ T cells in celiac disease. Proceedings of the National Academy of Sciences. 2013;110(32):13073-8.##De Re V, Magris R, Cannizzaro R. New insights into the pathogenesis of celiac disease. Frontiers in medicine. 2017;4:137.##Chai J. Introductory Chapter: Celiac Disease-Now and Then. Celiac Disease: IntechOpen; 2021. p. 1.##Caio G, Volta U, Sapone A, Leffler DA, De Giorgio R, Catassi C, Fasano A. Celiac disease: a comprehensive current review. BMC medicine. 2019;17:1-20.##Yu X, Vargas J, Green PH, Bhagat G. Innate lymphoid cells and celiac disease: current perspective. Cellular and Molecular Gastroenterology and Hepatology. 2021;11(3):803-14.##Gujral N, Freeman HJ, Thomson AB. Celiac disease: prevalence, diagnosis, pathogenesis and treatment. World journal of gastroenterology: WJG. 2012;18(42):6036.##Gnodi E, Meneveri R, Barisani D. Celiac disease: From genetics to epigenetics. World journal of gastroenterology. 2022;28(4):449.##Bakker OB, Ramírez-Sánchez AD, Borek ZA, de Klein N, Li Y, Modderman R, Kooy-Winkelaar Y, Johannesen MK, Matarese F, Martens JH. Potential impact of celiac disease genetic risk factors on T cell receptor signaling in gluten-specific CD4+ T cells. Scientific reports. 2021;11(1):9252.##Izzo V, Pinelli M, Tinto N, Esposito MV, Cola A, Sperandeo MP, Tucci F, Cocozza S, Greco L, Sacchetti L. Improving the estimation of celiac disease sibling risk by non-HLA genes. PloS one. 2011;6(11):e26920.##Van Belzen MJ, Meijer JW, Sandkuijl LA, Bardoel AF, Mulder CJ, Pearson PL, Houwen RH, Wijmenga C. A major non-HLA locus in celiac disease maps to chromosome 19. Gastroenterology. 2003;125(4):1032-41.##Marafini I, Imeneo MG, Monteleone G. The role of natural killer receptors in celiac disease. Immunome Research. 2017;13(1):1.##Girbovan A, Sur G, Samasca G, Lupan I. Dysbiosis a risk factor for celiac disease. Medical microbiology and immunology. 2017;206:83-91.##Bascuñán KA, Araya M, Roncoroni L, Doneda L, Elli L. Dietary gluten as a conditioning factor of the gut microbiota in celiac disease. Advances in Nutrition. 2020;11(1):160-74.##Taraghikhah N, Ashtari S, Asri N, Shahbazkhani B, Al-Dulaimi D, Rostami-Nejad M, Rezaei-Tavirani M, Razzaghi MR, Zali MR. An updated overview of spectrum of gluten-related disorders: clinical and diagnostic aspects. BMC gastroenterology. 2020;20:1-12.##Gandini A, Gededzha MP, De Maayer T, Barrow P, Mayne E. Diagnosing coeliac disease: A literature review. Human Immunology. 2021;82(12):930-6.##Sahin Y. Celiac disease in children: A review of the literature. World journal of clinical pediatrics. 2021;10(4):53.##Molder A, Balaban DV, Molder C-C, Jinga M, Robin A. Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images. Diagnostics. 2023;13(17):2780.##Tarar ZI, Zafar MU, Farooq U, Basar O, Tahan V, Daglilar E. The progression of celiac disease, diagnostic modalities, and treatment options. Journal of Investigative Medicine High Impact Case Reports. 2021;9:23247096211053702.##Schiepatti A, Savioli J, Vernero M, Borrelli de Andreis F, Perfetti L, Meriggi A, Biagi F. Pitfalls in the diagnosis of coeliac disease and Gluten-Related disorders. Nutrients. 2020;12(6):1711.##Jinga M, Popp A, Balaban DV, Dima A, Jurcut C. Physicians' attitude and perception regarding celiac disease: A questionnaire-based study. The Turkish journal of gastroenterology. 2018;29(4):419.##Rodrigo L. Celiac disease: a common unrecognized health problem with a very delayed diagnosis. MDPI; 2019. p. 9.##Emami M, Kouhestani S, Gholamrezaei A, Hashemi M, Mahzouni P, Raeisi M, Daghaghzadeh H, Daneshgar H. Prevalence of celiac disease in patients with irritable bowel syndrome. Govaresh. 2008;13(3):192-7.##Rodrigues M, Yonamine GH, Fernandes Satiro CA. Rate and determinants of non-adherence to a gluten-free diet and nutritional status assessment in children and adolescents with celiac disease in a tertiary Brazilian referral center: a cross-sectional and retrospective study. BMC gastroenterology. 2018;18(1):1-8.##https://doi.org/10.1186/s12876-018-0757-3##Czaja-Bulsa G, Bulsa M. Adherence to gluten-free diet in children with celiac disease. Nutrients. 2018;10(10):1424.##Hellman R. Gluten free diets-a challenge for the practicing physician. Missouri Medicine. 2020;117(2):119.##Singh P, Dennis M. Eliminating dietary gluten: Don't be a "Glutton for punishment". Springer; 2018. p. 1374-5.##Mulder CJ, Elli L, Lebwohl B, Makharia GK, Rostami K, Rubio-Tapia A, Schumann M, Tye-Din J, Zeitz J, Al-Toma A. Follow-Up of Celiac Disease in Adults:"When, What, Who, and Where". Nutrients. 2023;15(9):2048.##Fueyo-Díaz R, Gascón-Santos S, Magallón-Botaya R. I Can't Eat That! Sticking to a Gluten-Free Diet. Celiac Disease and Non-Celiac Gluten Sensitivity. 2017.##Aggarwal S, Lebwohl B, Green PH. Screening for celiac disease in average-risk and high-risk populations. Therapeutic advances in gastroenterology. 2012;5(1):37-47.##Cosnes J, Cellier C, Viola S, Colombel JF, Michaud L, Sarles J, Hugot JP, Ginies JL, Dabadie A, Mouterde O. Incidence of autoimmune diseases in celiac disease: protective effect of the gluten-free diet. Clinical Gastroenterology and Hepatology. 2008;6(7):753-8.##Gładyś K, Dardzińska J, Guzek M, Adrych K, Kochan Z, Małgorzewicz S. Expanded role of a dietitian in monitoring a gluten-free diet in patients with celiac disease: Implications for clinical practice. Nutrients. 2021;13(6):1859.##Möller SP, Hayes B, Wilding H, Apputhurai P, Tye-Din JA, Knowles SR. Systematic review: Exploration of the impact of psychosocial factors on quality of life in adults living with coeliac disease. Journal of Psychosomatic Research. 2021;147:110537.##Canova C, Rosato I, Marsilio I, Valiante F, Zorzetto V, Cataudella G, D'Odorico A, Zingone F. Quality of life and psychological disorders in coeliac disease: a prospective multicentre study. Nutrients. 2021;13(9):3233.##Coburn S, Rose M, Streisand R, Sady M, Parker M, Suslovic W, Weisbrod V, Kerzner B, Kahn I. Psychological needs and services in a pediatric multidisciplinary celiac disease clinic. Journal of clinical psychology in medical settings. 2020;27:433-43.##Valitutti F, Trovato CM, Montuori M, Cucchiara S. Pediatric celiac disease: follow-up in the spotlight. Advances in Nutrition. 2017;8(2):356-61.##Meijer CR, Auricchio R, Putter H, Castillejo G, Crespo P, Gyimesi J, Hartman C, Kolacek S, Koletzko S, Korponay-Szabo I. Prediction models for celiac disease development in children from high-risk families: Data from the PreventCD cohort. Gastroenterology. 2022;163(2):426-36.##Oliveira A, Trindade E, Tavares M, Lima R, Terra M, Dias JA. Celiac disease in first degree relatives of celiac children. Arquivos de Gastroenterologia. 2012;49:204-7.##Singh P, Singh AD, Ahuja V, Makharia GK. Who to screen and how to screen for celiac disease. World Journal of Gastroenterology. 2022;28(32):4493.##Tapkire MD, Arun V. A Survey on celiac disease prediction using AI Techniques.##Molder A, Balaban DV, Jinga M, Molder C-C. Current evidence on computer-aided diagnosis of celiac disease: Systematic review. Frontiers in Pharmacology. 2020;11:341.##Khaleghi S, Ju JM, Lamba A, Murray JA. The potential utility of tight junction regulation in celiac disease: focus on larazotide acetate. Therap Adv Gastroenterol. 2016;9(1):37-49.##Tye-Din JA, Galipeau HJ, Agardh D. Celiac Disease: A Review of Current Concepts in Pathogenesis, Prevention, and Novel Therapies. Front Pediatr. 2018;6:350.##López Casado MÁ, Lorite P, Ponce de León C, Palomeque T, Torres MI. Celiac Disease Autoimmunity. Archivum Immunologiae et Therapiae Experimentalis. 2018;66(6):423-30.##Larentzakis A, Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool. Pan African Medical Journal. 2021;38(1).##Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointestinal endoscopy. 2020;92(4):807-12.##Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-S40.##Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in healthcare: Elsevier; 2020. p. 25-60.##Ragavi V, Sheela AS, Kannaiyan GN, editors. Impact of Artificial Intelligence in the field of Health Care. Journal of Physics: Conference Series; 2021: IOP Publishing.##Fukui H, Nishida A, Matsuda S, Kira F, Watanabe S, Kuriyama M, Kawakami K, Aikawa Y, Oda N, Arai K. Usefulness of machine learning-based gut microbiome analysis for identifying patients with irritable bowels syndrome. Journal of clinical medicine. 2020;9(8):2403.##Patil A, Singh N. Artificial intelligence for Microbiota Analysis: Advances and Applications. 2023.##Akobeng AK, Singh P, Kumar M, Al Khodor S. Role of the gut microbiota in the pathogenesis of coeliac disease and potential therapeutic implications. European journal of nutrition. 2020;59:3369-90.##Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019;8(7):2328.##Méndez C, Carrasco M, Mora B, Araya M. Caracterización de la enfermedad celiaca en niños atendidos en hospitales públicos chilenos. Revista chilena de pediatría. 2018;89(6):709-17.##Salinari A, Machì M, Armas Diaz Y, Cianciosi D, Qi Z, Yang B, Ferreiro Cotorruelo MS, Villar SG, Dzul López LA, Battino M. The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases. 2023;11(3):97.##Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World journal of gastroenterology. 2020;26(46):7287.##Carreras J, editor Artificial intelligence analysis of celiac disease using an autoimmune discovery transcriptomic panel highlighted pathogenic genes including BTLA. Healthcare; 2022: MDPI.##Locke S, Bashall A, Al-Adely S, Moore J, Wilson A, Kitchen GB. Natural language processing in medicine: a review. Trends in Anaesthesia and Critical Care. 2021;38:4-9.##Ludvigsson JF, Pathak J, Murphy S, Durski M, Kirsch PS, Chute CG, Ryu E, Murray JA. Use of computerized algorithm to identify individuals in need of testing for celiac disease. Journal of the American Medical Informatics Association. 2013;20(e2):e306-e10.##Qin K, Li J, Fang Y, Xu Y, Wu J, Zhang H, Li H, Liu S, Li Q. Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis. Surgical endoscopy. 2022;36(1):16-31.##Patel V, Khan MN, Shrivastava A, Sadiq K, Ali SA, Moore SR, Brown DE, Syed S. Artificial intelligence applied to gastrointestinal diagnostics: a review. Journal of pediatric gastroenterology and nutrition. 2020;70(1):4.##Wimmer G, Vécsei A, Uhl A, editors. CNN transfer learning for the automated diagnosis of celiac disease. 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA); 2016: IEEE.##Vécsei A, Amann G, Hegenbart S, Liedlgruber M, Uhl A. Automated Marsh-like classification of celiac disease in children using local texture operators. Computers in Biology and Medicine. 2011;41(6):313-25.##Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition. 1996;29(1):51-9.##Chou R, Bougatsos C, Blazina I, Mackey K, Grusing S, Selph S. Screening for celiac disease: evidence report and systematic review for the US Preventive Services Task Force. Jama. 2017;317(12):1258-68.##Popp A, Kivelä L, Fuchs V, Kurppa K. Diagnosing celiac disease: towards wide-scale screening and serology-based criteria? Gastroenterology research and practice. 2019;2019.##Zammit SC, McAlindon ME, Greenblatt E, Maker M, Siegelman J, Leffler DA, Yardibi O, Raunig D, Brown T, Sidhu R. Quantification of celiac disease severity using video capsule endoscopy: a comparison of human experts and machine learning algorithms. Current Medical Imaging. 2023;19(12):1455.##Ciaccio EJ, Tennyson CA, Bhagat G, Lewis SK, Green PH. Implementation of a polling protocol for predicting celiac disease in videocapsule analysis. World journal of gastrointestinal endoscopy. 2013;5(7):313.##Tenório JM, Hummel AD, Cohrs FM, Sdepanian VL, Pisa IT, de Fátima Marin H. Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. International journal of medical informatics. 2011;80(11):793-802.##Amiri M, Li J, Hasan W. Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study. JMIR Formative Research. 2023;7:e46434.##Jnawali P, Kumar V, Tanwar B. Celiac disease: Overview and considerations for development of gluten-free foods. Food Science and Human Wellness. 2016;5(4):169-76.##Stefanidis K, Tsatsou D, Konstantinidis D, Gymnopoulos L, Daras P, Wilson-Barnes S, Hart K, Cornelissen V, Decorte E, Lalama E. PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets. Nutrients. 2022;14(20):4435.##Bond A, Mccay K, Lal S. Artificial Intelligence & Clinical Nutrition: what the future might have in store. Clinical Nutrition ESPEN. 2023.##Schiepatti A, Maimaris S, Lusetti F, Scalvini D, Minerba P, Cincotta M, Fazzino E, Biagi F. High prevalence of functional gastrointestinal disorders in celiac patients with persistent symptoms on a gluten-free diet: A 20-year follow-up study. Digestive Diseases and Sciences. 2023;68(8):3374-82.##Wieser H, Ruiz-Carnicer Á, Segura V, Comino I, Sousa C. Challenges of monitoring the gluten-free diet adherence in the management and follow-up of patients with celiac disease. Nutrients. 2021;13(7):2274.##Wieser H, Segura V, Ruiz-Carnicer Á, Sousa C, Comino I. Food safety and cross-contamination of gluten-free products: A narrative review. Nutrients. 2021;13(7):2244.##Guennouni M, Admou B, Bourrhouat A, Zogaam LG, Elmoumou L, Hilali A. Gluten contamination in labelled gluten-free, naturally gluten-free and meals in food services in low-, middle-and high-income countries: a systematic review and meta-analysis. British Journal of Nutrition. 2022;127(10):1528-42.##Jossa-Bastidas O, Sanchez AO, Bravo-Lamas L, Garcia-Zapirain B. IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques. Electronics. 2023;12(8):1916.##Okeke AG. Fourier Transform Infrared Spectroscopy (As A Rapid Method) Coupled With Machine Learning Approaches For Detection And Quantification Of Gluten Contaminations In Grain-Based Foods. 2020.##Pradana-López S, Pérez-Calabuig AM, Otero L, Cancilla JC, Torrecilla JS. Is my food safe?-AI-based classification of lentil flour samples with trace levels of gluten or nuts. Food Chemistry. 2022;386:132832.##Wessels M, Dolinsek J, Castillejo G, Donat E, Riznik P, Roca M, Valitutti F, Veenvliet A, Mearin ML. Follow-up practices for children and adolescents with celiac disease: results of an international survey. European Journal of Pediatrics. 2022:1-8.##Sandström O, Norström F, Carlsson A, Högberg L, van der Palz M, Stenhammar L, Webb C, Ivarsson A, Myléus A. Five-year follow-up of new cases after a coeliac disease mass screening. Archives of Disease in Childhood. 2022;107(6):596-600.##Piccialli F, Calabrò F, Crisci D, Cuomo S, Prezioso E, Mandile R, Troncone R, Greco L, Auricchio R. Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease. Scientific Reports. 2021;11(1):5683.## ##