Fri, Jul 4, 2025
[Archive]
Volume 15, Issue 3 ( Special Issue (AI in Medicine) - August 2023 2023)
Iranian Journal of Blood and Cancer 2023, 15(3): 42-59 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Yousefi A, Zandi A, Shojaeian F, Marzban M R, Tavakol M R, Abiri H, et al . How artificial intelligence is revolutionizing precision medicine and drug discovery?. Iranian Journal of Blood and Cancer 2023; 15 (3) :42-59
URL: http://ijbc.ir/article-1-1440-en.html
URL: http://ijbc.ir/article-1-1440-en.html
Amir-Mohammad Yousefi1 
, Ashkan Zandi *2 , Fatemeh Shojaeian3 , Mahmood Reza Marzban4 , Mohammad Reza Tavakol4 , Hamed Abiri4 , Mahsa Hojabri5 , Ghazal Kaviani4 , Sam Pournezhad6
, Ashkan Zandi *2 , Fatemeh Shojaeian3 , Mahmood Reza Marzban4 , Mohammad Reza Tavakol4 , Hamed Abiri4 , Mahsa Hojabri5 , Ghazal Kaviani4 , Sam Pournezhad6
1- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. ,zandi@gatecedu
3- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.
4- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
5- Institute of Human Virology, School of Medicine, University of Maryland, Baltimore, MD, USA.
6- Marcus Stroke and Neuroscience Center, Grady Memorial Hospital, Atlanta, GA, 30303, USA.
2- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. ,
3- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.
4- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
5- Institute of Human Virology, School of Medicine, University of Maryland, Baltimore, MD, USA.
6- Marcus Stroke and Neuroscience Center, Grady Memorial Hospital, Atlanta, GA, 30303, USA.
Abstract: (1815 Views)
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.
Keywords: Artificial intelligence in medicine, Precision medicine, Drug discovery, Machine learning, Clinical trials optimization
: Review Article |
Subject:
AI in Medicine
Received: 2023/07/1 | Accepted: 2023/08/29 | Published: 2023/09/6
Received: 2023/07/1 | Accepted: 2023/08/29 | Published: 2023/09/6
References
1. Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol. 2013;31(15):1803-5. [DOI:10.1200/JCO.2013.49.4799]
2. Strianese O, Rizzo F, Ciccarelli M, Galasso G, D'Agostino Y, Salvati A, Del Giudice C, Tesorio P, Rusciano MR. Precision and personalized medicine: how genomic approach improves the management of cardiovascular and neurodegenerative disease. Genes. 2020;11(7):747. [DOI:10.3390/genes11070747]
3. Malone ER, Oliva M, Sabatini PJ, Stockley TL, Siu LL. Molecular profiling for precision cancer therapies. Genome medicine. 2020;12(1):1-19. [DOI:10.1186/s13073-019-0703-1]
4. Chen R, Yang L, Goodison S, Sun Y. Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics. 2020;36(5):1476-83. [DOI:10.1093/bioinformatics/btz769]
5. Uddin M, Wang Y, Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. NPJ digital medicine. 2019;2(1):112. [DOI:10.1038/s41746-019-0191-0]
6. Seymour CW, Gomez H, Chang C-CH, Clermont G, Kellum JA, Kennedy J, Yende S, Angus DC. Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness. Critical Care. 2017;21:1-11. [DOI:10.1186/s13054-017-1836-5]
7. Wu P-Y, Cheng C-W, Kaddi CD, Venugopalan J, Hoffman R, Wang MD. -Omic and electronic health record big data analytics for precision medicine. IEEE Transactions on Biomedical Engineering. 2016;64(2):263-73. [DOI:10.1109/TBME.2016.2573285]
8. Azuaje F. Bioinformatics and biomarker discovery:" omic" data analysis for personalized medicine: John Wiley & Sons; 2011. [DOI:10.1002/9780470686423]
9. Dugger SA, Platt A, Goldstein DB. Drug development in the era of precision medicine. Nature reviews Drug discovery. 2018;17(3):183-96. [DOI:10.1038/nrd.2017.226]
10. Kühl N, Schemmer M, Goutier M, Satzger G. Artificial intelligence and machine learning. Electronic Markets. 2022;32(4):2235-44. [DOI:10.1007/s12525-022-00598-0]
11. Erickson BJ. Basic artificial intelligence techniques: machine learning and deep learning. Radiologic Clinics. 2021;59(6):933-40. [DOI:10.1016/j.rcl.2021.06.004]
12. Kunze KN, Karhade AV, Sadauskas AJ, Schwab JH, Levine BR. Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty. The Journal of Arthroplasty. 2020;35(8):2119-23. [DOI:10.1016/j.arth.2020.03.019]
13. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity. 2021;25:1315-60. [DOI:10.1007/s11030-021-10217-3]
14. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology. 2020;132(2):379-94. [DOI:10.1097/ALN.0000000000002960]
15. Hornung AL, Hornung CM, Mallow GM, Barajas JN, Rush III A, Sayari AJ, Galbusera F, Wilke H-J, Colman M, Phillips FM. Artificial intelligence in spine care: current applications and future utility. European Spine Journal. 2022;31(8):2057-81. [DOI:10.1007/s00586-022-07176-0]
16. Lee D, Seo H, Jung MW. Neural basis of reinforcement learning and decision making. Annual review of neuroscience. 2012;35:287-308. [DOI:10.1146/annurev-neuro-062111-150512]
17. Shinde PP, Shah S, editors. A review of machine learning and deep learning applications. 2018 Fourth international conference on computing communication control and automation (ICCUBEA); 2018: IEEE. [DOI:10.1109/ICCUBEA.2018.8697857]
18. Deng L, Platt J, editors. Ensemble deep learning for speech recognition. Proc interspeech; 2014. [DOI:10.21437/Interspeech.2014-433]
19. Deng L, Liu Y. Deep learning in natural language processing: Springer; 2018. [DOI:10.1007/978-981-10-5209-5]
20. Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Molecular informatics. 2016;35(1):3-14. [DOI:10.1002/minf.201501008]
21. Andrianov AM, Nikolaev GI, Shuldov NA, Bosko IP, Anischenko AI, Tuzikov AV. Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors. Journal of Biomolecular Structure and Dynamics. 2022;40(16):7555-73. [DOI:10.1080/07391102.2021.1905559]
22. Dastres R, Soori M. Artificial neural network systems. International Journal of Imaging and Robotics (IJIR). 2021;21(2):13-25.
23. Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. Journal of big data. 2015;2(1):1-21. [DOI:10.1186/s40537-014-0007-7]
24. Zhong B, Xing X, Love P, Wang X, Luo H. Convolutional neural network: Deep learning-based classification of building quality problems. Advanced Engineering Informatics. 2019;40:46-57. [DOI:10.1016/j.aei.2019.02.009]
25. Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181(1):92-101. [DOI:10.1016/j.cell.2020.03.022]
26. Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, Menden MP. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opinion on Drug Discovery. 2021;16(9):991-1007. [DOI:10.1080/17460441.2021.1918096]
27. Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Frontiers in Oncology. 2023;12:998222. [DOI:10.3389/fonc.2022.998222]
28. Khodadadian A, Darzi S, Haghi-Daredeh S, Sadat Eshaghi F, Babakhanzadeh E, Mirabutalebi SH, Nazari M. Genomics and transcriptomics: the powerful technologies in precision medicine. International Journal of General Medicine. 2020:627-40. [DOI:10.2147/IJGM.S249970]
29. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision medicine, AI, and the future of personalized health care. Clinical and translational science. 2021;14(1):86-93. [DOI:10.1111/cts.12884]
30. Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery. Trends in Pharmacological Sciences. 2023. [DOI:10.1016/j.tips.2023.06.010]
31. Qaiser T, Lee C-Y, Vandenberghe M, Yeh J, Gavrielides MA, Hipp J, Scott M, Reischl J. Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials. NPJ precision oncology. 2022;6(1):37. [DOI:10.1038/s41698-022-00275-7]
32. Ho D, Quake SR, McCabe ER, Chng WJ, Chow EK, Ding X, Gelb BD, Ginsburg GS, Hassenstab J, Ho C-M. Enabling technologies for personalized and precision medicine. Trends in biotechnology. 2020;38(5):497-518. [DOI:10.1016/j.tibtech.2019.12.021]
33. Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009-2018. Jama. 2020;323(9):844-53. [DOI:10.1001/jama.2020.1166]
34. Kraljevic S, Stambrook PJ, Pavelic K. Accelerating drug discovery: Although the evolution of '‐omics' methodologies is still in its infancy, both the pharmaceutical industry and patients could benefit from their implementation in the drug development process. EMBO reports. 2004;5(9):837-42. [DOI:10.1038/sj.embor.7400236]
35. Adebisi YA, Nwogu IB, Alaran AJ, Badmos AO, Bamgboye AO, Rufai BO, Okonji OC, Malik MO, Teibo JO, Abdalla SF. Revisiting the issue of access to medicines in Africa: challenges and recommendations. Public Health Challenges. 2022;1(2):e9. [DOI:10.1002/puh2.9]
36. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. British journal of pharmacology. 2011;162(6):1239-49. [DOI:10.1111/j.1476-5381.2010.01127.x]
37. Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou MM, Zhang B. Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions. Medicinal research reviews. 2021;41(3):1427-73. [DOI:10.1002/med.21764]
38. Zhu Y, Ouyang Z, Du H, Wang M, Wang J, Sun H, Kong L, Xu Q, Ma H, Sun Y. New opportunities and challenges of natural products research: When target identification meets single-cell multiomics. Acta Pharmaceutica Sinica B. 2022. [DOI:10.1016/j.apsb.2022.08.022]
39. 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. [DOI:10.1038/s41392-022-00994-0]
40. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ digital medicine. 2018;1(1):9. [DOI:10.1038/s41746-017-0015-z]
41. Álvarez-Machancoses Ó, Fernández-Martínez JL. Using artificial intelligence methods to speed up drug discovery. Expert opinion on drug discovery. 2019;14(8):769-77. [DOI:10.1080/17460441.2019.1621284]
42. Volkamer A, Riniker S, Nittinger E, Lanini J, Grisoni F, Evertsson E, Rodríguez-Pérez R, Schneider N. Machine learning for small molecule drug discovery in academia and industry. Artificial Intelligence in the Life Sciences. 2023;3:100056. [DOI:10.1016/j.ailsci.2022.100056]
43. Mak K-K, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug discovery today. 2019;24(3):773-80. [DOI:10.1016/j.drudis.2018.11.014]
44. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chemical reviews. 2019;119(18):10520-94. [DOI:10.1021/acs.chemrev.8b00728]
45. Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert opinion on drug discovery. 2021;16(9):977-89. [DOI:10.1080/17460441.2021.1883585]
46. Ahmed F, Soomro AM, Salih ARC, Samantasinghar A, Asif A, Kang IS, Choi KH. A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19. Biomedicine & Pharmacotherapy. 2022;153:113350. [DOI:10.1016/j.biopha.2022.113350]
47. Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. Journal of Chemical Information and Modeling. 2023;63(9):2628-43. [DOI:10.1021/acs.jcim.3c00200]
48. 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. Pharmaceuticals. 2023;16(6):891. [DOI:10.3390/ph16060891]
49. Delso G, Cirillo D, Kaggie JD, Valencia A, Metser U, Veit-Haibach P, editors. How to design AI-driven clinical trials in nuclear medicine. Seminars in nuclear medicine; 2021: Elsevier. [DOI:10.1053/j.semnuclmed.2020.09.003]
50. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health and Technology. 2023;13(2):203-13. [DOI:10.1007/s12553-023-00738-2]
51. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends in pharmacological sciences. 2019;40(8):577-91. [DOI:10.1016/j.tips.2019.05.005]
52. Chow R, Midroni J, Kaur J, Boldt G, Liu G, Eng L, Liu F-F, Haibe-Kains B, Lock M, Raman S. Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis. JNCI: Journal of the National Cancer Institute. 2023;115(4):365-74. [DOI:10.1093/jnci/djad013]
53. 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. [DOI:10.1200/CCI.19.00079]
54. Fountzilas E, Tsimberidou AM, Vo HH, Kurzrock R. Clinical trial design in the era of precision medicine. Genome medicine. 2022;14(1):1-27. [DOI:10.1186/s13073-022-01102-1]
55. Gardner S, Das S, Taylor K. AI enabled precision medicine: patient stratification, drug repurposing and combination therapies. Artificial intelligence in oncology drug discovery and development: IntechOpen; 2020. [DOI:10.5772/intechopen.92594]
56. Cascini F, Beccia F, Causio FA, Melnyk A, Zaino A, Ricciardi W. Scoping review of the current landscape of AI-based applications in clinical trials. Frontiers in Public Health. 2022;10:949377. [DOI:10.3389/fpubh.2022.949377]
57. Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, Jackson GP, Rhee K. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ digital medicine. 2021;4(1):54. [DOI:10.1038/s41746-021-00423-6]
58. Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics. 2018;34(13):i457-i66. [DOI:10.1093/bioinformatics/bty294]
59. Dey S, Luo H, Fokoue A, Hu J, Zhang P. Predicting adverse drug reactions through interpretable deep learning framework. BMC bioinformatics. 2018;19(21):1-13. [DOI:10.1186/s12859-018-2544-0]
60. Chen Z, Liu X, Hogan W, Shenkman E, Bian J. Applications of artificial intelligence in drug development using real-world data. Drug discovery today. 2021;26(5):1256-64. [DOI:10.1016/j.drudis.2020.12.013]
61. Kim H-S, Lee S, Kim JH. Real-world evidence versus randomized controlled trial: clinical research based on electronic medical records. Journal of Korean medical science. 2018;33(34). [DOI:10.3346/jkms.2018.33.e213]
62. Morales DR, Arlett P. RCTs and real world evidence are complementary, not alternatives. bmj. 2023;381. [DOI:10.1136/bmj.p736]
63. Koromina M, Pandi M-T, Patrinos GP. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. Omics: a journal of integrative biology. 2019;23(11):539-48. [DOI:10.1089/omi.2019.0151]
64. Prasad K, Kumar V. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2. Current Research in Pharmacology and Drug Discovery. 2021;2:100042. [DOI:10.1016/j.crphar.2021.100042]
65. Tang Y, Eng C. PTEN autoregulates its expression by stabilization of p53 in a phosphatase-independent manner. Cancer research. 2006;66(2):736-42. [DOI:10.1158/0008-5472.CAN-05-1557]
66. Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal. 2020;18:784-90. [DOI:10.1016/j.csbj.2020.03.025]
67. Winters B, Custer J, Galvagno SM, Colantuoni E, Kapoor SG, Lee H, Goode V, Robinson K, Nakhasi A, Pronovost P. Diagnostic errors in the intensive care unit: a systematic review of autopsy studies. BMJ quality & safety. 2012;21(11):894-902. [DOI:10.1136/bmjqs-2012-000803]
68. 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 Communications. 2021;41(11):1100-15. [DOI:10.1002/cac2.12215]
69. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of ambient intelligence and humanized computing. 2022:1-28. [DOI:10.1007/s12652-021-03612-z]
70. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology. 2022;39(8):120. [DOI:10.1007/s12032-022-01711-1]
71. Nichols JA, Herbert Chan HW, Baker MA. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophysical reviews. 2019;11:111-8. [DOI:10.1007/s12551-018-0449-9]
72. Kaur C, Garg U. Artificial intelligence techniques for cancer detection in medical image processing: A review. Materials Today: Proceedings. 2021.
73. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature medicine. 2018;24(10):1559-67. [DOI:10.1038/s41591-018-0177-5]
74. Nomura A, Yamamoto S, Hayakawa Y, Taniguchi K, Higashitani T, Aono D, Kometani M, Yoneda T. SAT-LB121 development of a machine-learning method for predicting new onset of diabetes mellitus: a retrospective analysis of 509,153 annual specific health checkup records. Journal of the Endocrine Society. 2020;4(Supplement_1):SAT-LB121. [DOI:10.1210/jendso/bvaa046.2194]
75. Kwon J-m, Kim K-H, Jeon K-H, Lee SY, Park J, Oh B-H. Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography. Scandinavian journal of trauma, resuscitation and emergency medicine. 2020;28:1-10. [DOI:10.1186/s13049-020-00791-0]
76. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health affairs. 2014;33(7):1123-31. [DOI:10.1377/hlthaff.2014.0041]
77. Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging machine learning and artificial intelligence to improve peripheral artery disease detection, treatment, and outcomes. Circulation research. 2021;128(12):1833-50. [DOI:10.1161/CIRCRESAHA.121.318224]
78. Saifi S, Taylor AJ, Allen J, Hendel R. The use of a learning community and online evaluation of utilization for SPECT myocardial perfusion imaging. JACC: Cardiovascular Imaging. 2013;6(7):823-9. [DOI:10.1016/j.jcmg.2013.01.012]
79. Qureshi R, Irfan M, Gondal TM, Khan S, Wu J, Hadi MU, Heymach J, Le X, Yan H, Alam T. AI in Drug Discovery and its Clinical Relevance. Heliyon. 2023. [DOI:10.1016/j.heliyon.2023.e17575]
80. Achenbach J, Tiikkainen P, Franke L, Proschak E. Computational tools for polypharmacology and repurposing. Future medicinal chemistry. 2011;3(8):961-8. [DOI:10.4155/fmc.11.62]
81. Liu Q, Huang R, Hsieh J, Zhu H, Tiwari M, Liu G, Jean D, ElZarrad MK, Fakhouri T, Berman S. Landscape analysis of the application of artificial intelligence and machine learning in regulatory submissions for drug development from 2016 to 2021. Clinical pharmacology and therapeutics. 2023;113(4):771-4. [DOI:10.1002/cpt.2668]
82. Blasiak A, Truong AT, Remus A, Hooi L, Seah SGK, Wang P, Chye DH, Lim APC, Ng KT, Teo ST. The IDentif. AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens. NPJ Digital Medicine. 2022;5(1):83. [DOI:10.1038/s41746-022-00627-4]
83. Zhu X, Huang W, Lu H, Wang Z, Ni X, Hu J, Deng S, Tan Y, Li L, Zhang M. A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters. Scientific Reports. 2021;11(1):5568. [DOI:10.1038/s41598-021-85157-x]
84. 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. [DOI:10.1038/s41598-021-84951-x]
85. Patil S, Shankar H. Transforming Healthcare: Harnessing the Power of AI in the Modern Era. International Journal of Multidisciplinary Sciences and Arts. 2023;2(1):60-70.
86. Sensmeier J. Harnessing the power of artificial intelligence. Nursing management. 2017;48(11):14-9. [DOI:10.1097/01.NUMA.0000526062.69220.41]
87. Sng GGR, Tung JYM, Lim DYZ, Bee YM. Potential and pitfalls of ChatGPT and natural-language artificial intelligence models for diabetes education. Diabetes Care. 2023;46(5):e103-e5. [DOI:10.2337/dc23-0197]
88. Sun J, Dong Q-X, Wang S-W, Zheng Y-B, Liu X-X, Lu T-S, Yuan K, Shi J, Hu B, Lu L. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian Journal of Psychiatry. 2023:103705. [DOI:10.1016/j.ajp.2023.103705]
89. Loppini M, Gambaro FM, Chiappetta K, Grappiolo G, Bianchi AM, Corino VD. Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach. Bioengineering. 2022;9(7):288. [DOI:10.3390/bioengineering9070288]
90. Ahmad LG, Eshlaghy A, Poorebrahimi A, Ebrahimi M, Razavi A. Using three machine learning techniques for predicting breast cancer recurrence. J Health Med Inform. 2013;4(124):3.
91. Hassan AM, Rajesh A, Asaad M, Nelson JA, Coert JH, Mehrara BJ, Butler CE. Artificial intelligence and machine learning in prediction of surgical complications: Current state, applications, and implications. The American Surgeon. 2023;89(1):25-30. [DOI:10.1177/00031348221101488]
92. Ellahham S. Artificial intelligence: the future for diabetes care. The American journal of medicine. 2020;133(8):895-900. [DOI:10.1016/j.amjmed.2020.03.033]
93. Bharti U, Bajaj D, Batra H, Lalit S, Lalit S, Gangwani A, editors. Medbot: Conversational artificial intelligence powered chatbot for delivering tele-health after covid-19. 2020 5th international conference on communication and electronics systems (ICCES); 2020: IEEE. [DOI:10.1109/ICCES48766.2020.9137944]
94. Haleem A, Javaid M, Singh RP, Suman R. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors international. 2021;2:100117. [DOI:10.1016/j.sintl.2021.100117]
95. Xie Y, Lu L, Gao F, He S-j, Zhao H-j, Fang Y, Yang J-m, An Y, Ye Z-w, Dong Z. Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare. Current Medical Science. 2021;41:1123-33. [DOI:10.1007/s11596-021-2485-0]
96. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim H-C, Jeste DV. Artificial intelligence for mental health and mental illnesses: an overview. Current psychiatry reports. 2019;21:1-18. [DOI:10.1007/s11920-019-1094-0]
97. Kim D-H, Kim TJ, Wang X, Kim M, Quan Y-J, Oh JW, Min S-H, Kim H, Bhandari B, Yang I. Smart machining process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology. 2018;5:555-68. [DOI:10.1007/s40684-018-0057-y]
98. Budach L, Feuerpfeil M, Ihde N, Nathansen A, Noack N, Patzlaff H, Naumann F, Harmouch H. The effects of data quality on machine learning performance. arXiv preprint arXiv:220714529. 2022.
99. Du J, Zhou L. Improving financial data quality using ontologies. Decision Support Systems. 2012;54(1):76-86. [DOI:10.1016/j.dss.2012.04.016]
100. Star SL. The structure of ill-structured solutions: Boundary objects and heterogeneous distributed problem solving. Distributed artificial intelligence: Elsevier; 1989. p. 37-54. [DOI:10.1016/B978-1-55860-092-8.50006-X]
101. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019;6(2):94. [DOI:10.7861/futurehosp.6-2-94]
102. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics. 2021;22(1):1-5. [DOI:10.1186/s12910-021-00687-3]
103. Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello C-P, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digital Medicine. 2023;6(1):111. [DOI:10.1038/s41746-023-00852-5]
104. Longoni C, Bonezzi A, Morewedge CK. Resistance to medical artificial intelligence. Journal of Consumer Research. 2019;46(4):629-50. [DOI:10.1093/jcr/ucz013]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
- Iranian Journal of Blood and Cancer
- Journal Tel: +989369936116
- Publisher Tel: +982122275917
- Mail: Info@ijbc.ir
