Volume 17, Issue 3 (September-2025 2025)                   Iranian Journal of Blood and Cancer 2025, 17(3): 46-61 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Haghshenas Z, Shokri Garjan H, Moghassem A, Vahedi P, Tabatabavakili Y, Hosseinzadeh D, et al . The Role of Artificial Intelligence in Shaping the Future of Hematological Diagnosis and Treatment. Iranian Journal of Blood and Cancer 2025; 17 (3) :46-61
URL: http://ijbc.ir/article-1-1786-en.html
The Role of Artificial Intelligence in Shaping the Future of Hematological Diagnosis and Treatment
Zahra Haghshenas1 , Hassan Shokri Garjan2 , Atefeh Moghassem3 , Pooya Vahedi4 , Yasmin Tabatabavakili5 , Diana Hosseinzadeh6 , Goli Asgari7 , Elham Nazari *8 , Tahmine Aldaghi9
1- School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Medical Informatics, School of Allied Medical Science, Urmia University of Medical Science, Urmia, Iran.
3- Laboratory Hematology and Blood Bank Department, School of Allied Medical Science, Shahid Beheshti University of Medical Science, Tehran, Iran.
4- School of Medicine, Shahid Beheshti University of Medical Science, Tehran, Iran.
5- School of Nursing and Midwifery, Shahid Beheshti University of Medical Science, Tehran, Iran.
6- Third faculty of medicine, Charles University, Prague, Czech Republic.
7- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
8- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. , Elham.Nazari@sbmu.ac.ir
9- Institute of Biophysics and Informatics, First Faculty of Medicine, Charles University, Prague, Czech Republic
Abstract:   (549 Views)
Hematological disorders continue to pose significant challenges in clinical practice due to their complexity and potential for severe outcomes. This review provides a comprehensive overview of the role of Artificial Intelligence (AI) in enhancing the diagnosis and treatment of these conditions. Drawing on 177 studies published between 2012 and 2025 from PubMed and Google Scholar, the review examines fundamental concepts of AI and machine learning, their applications in diagnostic and therapeutic processes, and the challenges and limitations associated with their clinical implementation. The findings highlight the potential of AI to improve diagnostic accuracy, optimize treatment strategies, and support decision-making in hematology. By synthesizing current knowledge, this study underscores the importance of integrating AI into research and clinical practice and offers insights into future directions for advancing patient care in hematological disorders.
Keywords: Artificial intelligence, Machine learning, Hematological disorders
Full-Text [PDF 656 kb]   (1081 Downloads)    
: Review Article | Subject: Adults Hematology & Oncology
Received: 2025/08/10 | Accepted: 2025/09/25 | Published: 2025/09/30
References
1. Pan T, Zhang J, Wang X, Song Y. Global burden and trends of hematologic malignancies based on Global Cancer Observatory 2022 and Global Burden of Disease 2021. Exp Hematol Oncol. 2025;14(1):98. [DOI:10.1186/s40164-025-00684-x]
2. Sun D, Chen W, He J, He Y, Jiang H, Jiang H, et al. A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters. BMC Medical Informatics and Decision Making. 2025;25(1). [DOI:10.1186/s12911-025-02892-1]
3. Collaborators GBDA. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990-2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(9):e713-e34.
4. Liao H, Zhang F, Chen F, Li Y, Sun Y, Sloboda DD, et al. Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects. Acta Pharmaceutica Sinica B. 2025. [DOI:10.1016/j.apsb.2025.05.036]
5. Isa A, Yusoff NM, Hamdani HY, Nazman HNM, Alhawamdeh M, Abeden Z, et al. Application of artificial intelligence in the diagnosis of haematological disorders. Biomedical Research and Therapy. 2024;11(12):6989-7002. [DOI:10.15419/bmrat.v11i12.945]
6. Wang S-X, Huang Z-F, Li J, Wu Y, Du J, Li T. Optimization of diagnosis and treatment of hematological diseases via artificial intelligence. Frontiers in Medicine. 2024;11. [DOI:10.3389/fmed.2024.1487234]
7. 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. Journal of Medical Internet Research. 2022;24(7). [DOI:10.2196/36490]
8. Ekpe N, Odii A, Adene G, Aguzu L. Applications of Artificial Intelligence and Machine Learning in Clinical Practice. 2025;3:1-15.
9. T S, B GJ. From algorithm to applications: Artificial intelligence - A future prospective in medicine. Sri Ramachandra Journal of Health Sciences. 2025;4:44-52. [DOI:10.25259/SRJHS_16_2024]
10. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328-31. [DOI:10.4103/jfmpc.jfmpc_440_19]
11. Srivastava R. Applications of Artificial Intelligence in Medicine. Exploratory Research and Hypothesis in Medicine. 2023;9(2):138-46. [DOI:10.14218/ERHM.2023.00048]
12. Eskandar K. Artificial Intelligence in Healthcare: Explore the Applications of AI in Various Medical Domains, Such as Medical Imaging, Diagnosis, Drug Discovery, and Patient Care. 2023;1:37-53.
13. Walter W, Pohlkamp C, Meggendorfer M, Nadarajah N, Kern W, Haferlach C, et al. Artificial intelligence in hematological diagnostics: Game changer or gadget? Blood Reviews. 2023;58. [DOI:10.1016/j.blre.2022.101019]
14. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education. 2023;23(1). [DOI:10.1186/s12909-023-04698-z]
15. Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals. 2020;135:109864. [DOI:10.1016/j.chaos.2020.109864]
16. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation (Camb). 2021;2(4):100179. [DOI:10.1016/j.xinn.2021.100179]
17. Akanksha M. A Comprehensive Review of Artificial Intelligence and Machine Learning : Concepts, Trends, and Applications. International Journal of Scientific Research in Science and Technology. 2024;11(5):126-42. [DOI:10.32628/IJSRST2411587]
18. Rojas-Carabali W, Agrawal R, Gutierrez-Sinisterra L, Baxter SL, Cifuentes-González C, Wei YC, et al. Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician. Asia Pac J Ophthalmol (Phila). 2024;13(4):100084. [DOI:10.1016/j.apjo.2024.100084]
19. Furizal F, Ma'arif A, Rifaldi D. Application of Machine Learning in Healthcare and Medicine: A Review. Journal of Robotics and Control (JRC). 2023;4(5):621-31. [DOI:10.18196/jrc.v4i5.19640]
20. Rashidi HH, Pantanowitz J, Hanna MG, Tafti AP, Sanghani P, Buchinsky A, et al. Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics. Modern Pathology. 2025;38(4). [DOI:10.1016/j.modpat.2024.100688]
21. Taye MM. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers. 2023;12(5):91. [DOI:10.3390/computers12050091]
22. Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M, et al. What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine. Diagnostics. 2023;13(15). [DOI:10.3390/diagnostics13152582]
23. Bali J, Bali O. Artificial Intelligence Applications in Medicine: A Rapid Overview of Current Paradigms. EMJ Innovations. 2020:73-81. [DOI:10.33590/emjinnov/19-00167]
24. Trezza A, Visibelli A, Roncaglia B, Spiga O, Santucci A. Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI. Applied Sciences. 2024;14(20):9305. [DOI:10.3390/app14209305]
25. Tettey-Engmann F, Parupelli SK, Bauer SR, Bhattarai N, Desai S. Advances in Artificial Intelligence-Based Medical Devices for Healthcare Applications. Biomedical Materials & Devices. 2025. [DOI:10.1007/s44174-025-00379-1]
26. Woodman RJ, Mangoni AA. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clinical and Experimental Research. 2023;35(11):2363-97. [DOI:10.1007/s40520-023-02552-2]
27. Rieu Z, Kim J, Kim RE, Lee M, Lee MK, Oh SW, et al. Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI. Brain Sciences. 2021;11(6):720. [DOI:10.3390/brainsci11060720]
28. Sarker IH. AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Comput Sci. 2022;3(2):158. [DOI:10.1007/s42979-022-01043-x]
29. Anzabi Zadeh S, Street WN, Thomas BW. Optimizing warfarin dosing using deep reinforcement learning. J Biomed Inform. 2023;137:104267. [DOI:10.1016/j.jbi.2022.104267]
30. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointestinal Endoscopy. 2020;92(4):807-12. [DOI:10.1016/j.gie.2020.06.040]
31. Grzybowski A, Brona P, Lim G, Ruamviboonsuk P, Tan GSW, Abramoff M, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond). 2020;34(3):451-60. https://doi.org/10.1038/s41433-019-0566-0 [DOI:10.1038/s41433-019-0728-0]
32. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236-46. [DOI:10.1093/bib/bbx044]
33. Chakraborty C, Bhattacharya M, Pal S, Lee S-S. From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Current Research in Biotechnology. 2024;7. [DOI:10.1016/j.crbiot.2023.100164]
34. Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):246-53. [DOI:10.1007/978-3-642-40763-5_31]
35. Chen R, Stewart WF, Sun J, Ng K, Yan X. Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data. Circulation: Cardiovascular Quality and Outcomes. 2019;12(10). [DOI:10.1161/CIRCOUTCOMES.118.005114]
36. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361-70. [DOI:10.1093/jamia/ocw112]
37. Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, et al. Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review. Journal of Biomedical Informatics. 2021;115:103671. [DOI:10.1016/j.jbi.2020.103671]
38. Joshi D, Singh B, Kalonia S, Kaushik AK, Goyal N, Maggu S, editors. Predictive Modeling of Cardiovascular Disease Using Feedforward Neural Networks. Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics; 2025 2025//; Singapore: Springer Nature Singapore. [DOI:10.1007/978-981-97-8669-5_15]
39. Tran T, Nguyen TD, Phung D, Venkatesh S. Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). J Biomed Inform. 2015;54:96-105. [DOI:10.1016/j.jbi.2015.01.012]
40. Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification2013.
41. Reis TC. Deep learning in oncology: Transforming cancer diagnosis, prognosis, and treatment. Emerging Trends in Drugs, Addictions, and Health. 2025;5. [DOI:10.1016/j.etdah.2025.100171]
42. Ucucu S, Azik F. Artificial intelligence-driven diagnosis of beta-thalassemia minor & iron deficiency anemia using machine learning models. J Med Biochem. 2024;43(1):11-8. [DOI:10.5937/jomb0-38779]
43. Kitaw B, Asefa C, Legese F. Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context. BMC Public Health. 2024;24(1):3500. [DOI:10.1186/s12889-024-21039-x]
44. Vohra R, Hussain A, Dudyala AK, Pahareeya J, Khan W. Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting. PLoS One. 2022;17(7):e0269685. [DOI:10.1371/journal.pone.0269685]
45. Mansour M, Donmez TB, Kutlu M, Mahmud S. Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks. Front Big Data. 2023;6:1291329. [DOI:10.3389/fdata.2023.1291329]
46. Ramzan M, Sheng J, Saeed MU, Wang B, Duraihem FZ. Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms. Vis Comput Ind Biomed Art. 2024;7(1):18. [DOI:10.1186/s42492-024-00169-4]
47. 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. [DOI:10.1186/s13040-023-00319-z]
48. Sehar N, Krishnamoorthi N, Vinoth Kumar C. Deep Learning Model-Based Detection of Anemia from Conjunctiva Images. Healthc Inform Res. 2025;31(1):57-65. [DOI:10.4258/hir.2025.31.1.57]
49. 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). [DOI:10.3390/healthcare11050697]
50. Tyas DA, Hartati S, Harjoko A, Ratnaningsih T. Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases. IEEE Access. 2020;8:69849-60. [DOI:10.1109/ACCESS.2020.2983155]
51. Aszhari FR, Rustam Z, Subroto F, Semendawai AS. Classification of thalassemia data using random forest algorithm. 2020;1490(1). [DOI:10.1088/1742-6596/1490/1/012050]
52. Eckardt JN, Middeke JM, Riechert S, Schmittmann T, Sulaiman AS, Kramer M, et al. Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears. Leukemia. 2022;36(1):111-8. [DOI:10.1038/s41375-021-01408-w]
53. Nazari E, Farzin AH, Aghemiri M, Avan A, Tara M, Tabesh H. Deep Learning for Acute Myeloid Leukemia Diagnosis. J Med Life. 2020;13(3):382-7. [DOI:10.25122/jml-2019-0090]
54. Sampathila N, Chadaga K, Goswami N, Chadaga RP, Pandya M, Prabhu S, et al. Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images. Healthcare (Basel). 2022;10(10). [DOI:10.3390/healthcare10101812]
55. Rezayi S, Mohammadzadeh N, Bouraghi H, Saeedi S, Mohammadpour A. Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods. Comput Intell Neurosci. 2021;2021:5478157. [DOI:10.1155/2021/5478157]
56. El Houby EMF. Acute lymphoblastic leukemia diagnosis using machine learning techniques based on selected features. Sci Rep. 2025;15(1):28056. [DOI:10.1038/s41598-025-12361-4]
57. Eckardt JN, Schmittmann T, Riechert S, Kramer M, Sulaiman AS, Sockel K, et al. Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears. BMC Cancer. 2022;22(1):201. [DOI:10.1186/s12885-022-09307-8]
58. Manescu P, Narayanan P, Bendkowski C, Elmi M, Claveau R, Pawar V, et al. Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning. Sci Rep. 2023;13(1):2562. [DOI:10.1038/s41598-023-29160-4]
59. Sidhom JW, Siddarthan IJ, Lai BS, Luo A, Hambley BC, Bynum J, et al. Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features. NPJ Precis Oncol. 2021;5(1):38. [DOI:10.1038/s41698-021-00179-y]
60. Shaabanpour Aghamaleki F, Mollashahi B, Nosrati M, Moradi A, Sheikhpour M, Movafagh A. Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia. Cureus. 2019;11(2):e4004. [DOI:10.7759/cureus.4004]
61. Huang F, Guang P, Li F, Liu X, Zhang W, Huang W. AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research. Medicine (Baltimore). 2020;99(45):e23154. [DOI:10.1097/MD.0000000000023154]
62. Achi HE, Belousova T, Chen L, Wahed A, Wang I, Hu Z, et al. Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning. 2018.
63. Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, et al. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020;11(1):6004. https://doi.org/10.1038/s41467-024-49660-9 [DOI:10.1038/s41467-020-19817-3]
64. Zhang Y, Deng Y, Zou Q, Jing B, Cai P, Tian X, et al. Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging. Cell Rep Med. 2024;5(5):101551. [DOI:10.1016/j.xcrm.2024.101551]
65. Kong H, Zhu H, Zheng X, Jiang M, Chen L, Lan L, et al. Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma. Front Immunol. 2022;13:919012. [DOI:10.3389/fimmu.2022.919012]
66. Delrue C, Hofmans M, Van Dorpe J, Van der Linden M, Van Gaever Z, Kerre T, et al. Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections. Commun Biol. 2024;7(1):1419. [DOI:10.1038/s42003-024-07111-7]
67. Peng T, Liu L, Liu F, Ding L, Liu J, Zhou H, et al. Machine learning-based infection prediction model for newly diagnosed multiple myeloma patients. Front Neuroinform. 2022;16:1063610. [DOI:10.3389/fninf.2022.1063610]
68. Yan W, Shi H, He T, Chen J, Wang C, Liao A, et al. Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma. Front Oncol. 2021;11:608191. [DOI:10.3389/fonc.2021.608191]
69. Chen X, Zhang Y, Li X, Yang Z, Liu A, Yu X. Diagnosis and staging of multiple myeloma using serum-based laser-induced breakdown spectroscopy combined with machine learning methods. Biomed Opt Express. 2021;12(6):3584-96. [DOI:10.1364/BOE.421333]
70. 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. Front Oncol. 2022;12:844978. [DOI:10.3389/fonc.2022.844978]
71. Nagao A, Inagaki Y, Nogami K, Yamasaki N, Iwasaki F, Liu Y, et al. Artificial intelligence-assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection. Res Pract Thromb Haemost. 2024;8(4):102439. [DOI:10.1016/j.rpth.2024.102439]
72. Tyrrell PN, Alvarez-Roman MT, Bakeer N, Brand-Staufer B, Jimenez-Yuste V, Kras S, et al. Utilizing artificial intelligence for the detection of hemarthrosis in hemophilia using point-of-care ultrasonography. Res Pract Thromb Haemost. 2024;8(8):102602. [DOI:10.1016/j.rpth.2024.102602]
73. Ferreira MV, Nogueira T, Rios RA, Lopes TJS. A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A. Front Bioinform. 2023;3:1152039. [DOI:10.3389/fbinf.2023.1152039]
74. Bibi N, Sikandar M, Ud Din I, Almogren A, Ali S. IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. J Healthc Eng. 2020;2020:6648574. [DOI:10.1155/2020/6648574]
75. Dasariraju S, Huo M, McCalla S. Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm. Bioengineering (Basel). 2020;7(4). [DOI:10.3390/bioengineering7040120]
76. Venezian Povoa L, Ribeiro CHC, Silva ITd. Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response. PLoS One. 2021;16(7):e0254596. [DOI:10.1371/journal.pone.0254596]
77. Ferle M, Grieb N, Kreuz M, Ader J, Goldschmidt H, Mai EK, et al. Predicting progression events in multiple myeloma from routine blood work. NPJ digital medicine. 2025;8(1):231. https://doi.org/10.1038/s41746-025-01719-7 [DOI:10.1038/s41746-025-01636-9]
78. Karathanasis N, Spyrou GM. Machine Learning Models for Predicting Multiple Myeloma Staging and MGUS Progression Using Gene Expression Data. bioRxiv. 2024:2024.11. 12.623149. [DOI:10.1101/2024.11.12.623149]
79. Qin Y. Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns. Scientific Reports. 2024;14:17874. [DOI:10.1038/s41598-024-68755-3]
80. Kosvyra Α, Karadimitris Α, Papaioannou Μ, Chouvarda I. Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia. Computers in Biology and Medicine. 2024;178:108735. [DOI:10.1016/j.compbiomed.2024.108735]
81. Mitra A, Kumar A, Amdare NP, Pathak R. Current landscape of cancer immunotherapy: harnessing the immune arsenal to overcome immune evasion. Biology. 2024;13(5):307. [DOI:10.3390/biology13050307]
82. Zhang Y. Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging. Cell Reports Medicine. 2024;5:101551. [DOI:10.1016/j.xcrm.2024.101551]
83. Naji H, Hahn P, Pisula JI, Ugliano S, Simon A, Büttner R, et al. Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma. BJC Reports. 2025;3(1):34. [DOI:10.1038/s44276-025-00147-0]
84. Ahmad W, Shahzad AR, Amin MA, Bangyal WH, Alahmadi TJ, Khan SH. Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences. PLoS One. 2025;20(6):e0321761. [DOI:10.1371/journal.pone.0321761]
85. Hauser RG. A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data. Am J Clin Pathol. 2021;156:1142-8. [DOI:10.1093/ajcp/aqab086]
86. Ram M. Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review. BMC Cancer. 2024;24:1026. [DOI:10.1186/s12885-024-12764-y]
87. Ai D, Cui C, Tang Y, Wang Y, Zhang N, Zhang C, et al. Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A. Thrombosis Research. 2023;232:43-53. [DOI:10.1016/j.thromres.2023.10.012]
88. Allegra A, Tonacci A, Sciaccotta R, Genovese S, Musolino C, Pioggia G, et al. Machine learning and deep learning applications in multiple myeloma diagnosis, prognosis, and treatment selection. Cancers. 2022;14(3):606. [DOI:10.3390/cancers14030606]
89. Abdel-Rehim A, Orhobor O, Griffiths G, Soldatova L, King RD. Establishing predictive machine learning models for drug responses in patient derived cell culture. npj Precision Oncology. 2025;9(1):180. [DOI:10.1038/s41698-025-00937-2]
90. Azarkhish I. Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data. J Med Syst. 2012;36:2057-61. [DOI:10.1007/s10916-011-9668-3]
91. Rezayi S. Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods. Computational Intelligence and Neuroscience. 2021;2021:5478157. [DOI:10.1155/2021/5478157]
92. Cheng ZJ. Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia. BMC Cancer. 2024;24:993. [DOI:10.1186/s12885-024-12646-3]
93. Alhajahjeh A, Nazha A. Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes. Current Hematologic Malignancy Reports. 2024;19:9-17. [DOI:10.1007/s11899-023-00716-5]
94. Azarkhish I, Raoufy MR, Gharibzadeh S. Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data. Journal of medical systems. 2012;36(3):2057-61. [DOI:10.1007/s10916-011-9668-3]
95. Wang V, Gauthier M, Decot V, Reppel L, Bensoussan D. Systematic review on CAR-T cell clinical trials up to 2022: academic center input. Cancers. 2023;15(4):1003. [DOI:10.3390/cancers15041003]
96. Qu Y, Huang K, Yin M, Zhan K, Liu D, Yin D, et al. CRISPR-GPT for agentic automation of gene-editing experiments. Nature Biomedical Engineering. 2025:1-14. [DOI:10.1038/s41551-025-01463-z]
97. Abbaszadeh A, Shahlai A. Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety. arXiv preprint arXiv:250820130. 2025.
98. Cai S. Artificial intelligence for improving sickle cell retinopathy diagnosis and management. Eye. 2021;35:2675-84. [DOI:10.1038/s41433-021-01556-4]
99. Boretti A. The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy. Computers in biology and medicine. 2024;182:109137. [DOI:10.1016/j.compbiomed.2024.109137]
100. Dermawan D, Alotaiq N. From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes. Pharmaceuticals. 2025;18(7):981. [DOI:10.3390/ph18070981]
101. Chihara D, Kane MJ, Flowers CR, Hobbs BP. Utilizing Synthetic Individual Patient Level Cohorts from Machine Learning on Clinical Trials to Define Endpoints for Bispecific Trials in Large B-Cell Lymphoma. Blood. 2024;144:4462. [DOI:10.1182/blood-2024-193578]
102. D'Amico S. Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology. JCO Clinical Cancer Informatics. 2023;7:e2300021.
103. Li L, Peng M, Zou Y, Li Y, Qiao P. The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection. Frontiers in Medicine. 2025;Volume 12 - 2025. [DOI:10.3389/fmed.2025.1514931]
104. Obeagu EI. Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence. Annals of Medicine and Surgery. 2025;87(6):3445-57. [DOI:10.1097/MS9.0000000000003227]
105. Karako K. Artificial intelligence applications in rare and intractable diseases: Advances, challenges, and future directions. Intractable Rare Dis Res. 2025;14(2):88-92. [DOI:10.5582/irdr.2025.01030]
106. Wang J. Deep Learning in Hematology: From Molecules to Patients. Clin Hematol Int. 2024;6(4):19-42. [DOI:10.46989/001c.124131]
107. Yang Y, Sun K, Gao Y, Wang K, Yu G. Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance. Diagnostics. 2023;13(19):3115. [DOI:10.3390/diagnostics13193115]
108. Meng J, Wu M, Shi F, Xie Y, Wang H, Guo Y. Medical laboratory data-based models: opportunities, obstacles, and solutions. Journal of Translational Medicine. 2025;23(1):823. [DOI:10.1186/s12967-025-06802-x]
109. Kang C, Lee C, Song H, Ma M, Pereira S, editors. Variability matters: Evaluating inter-rater variability in histopathology for robust cell detection. European Conference on Computer Vision; 2022: Springer. [DOI:10.1007/978-3-031-25082-8_37]
110. Marrón-Esquivel JM, Duran-Lopez L, Linares-Barranco A, Dominguez-Morales JP. A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer. Comput Biol Med. 2023;159:106856. [DOI:10.1016/j.compbiomed.2023.106856]
111. Cazzaniga G, Del Carro F, Eccher A, Becker JU, Gambaro G, Rossi M, et al. Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices. J Imaging Inform Med. 2025;38(2):1112-9. [DOI:10.1007/s10278-024-01248-x]
112. Wilm F, Bertram CA, Marzahl C, Bartel A, Donovan TA, Assenmacher C-A, et al., editors. Influence of inter-annotator variability on automatic mitotic figure assessment. Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021; 2021: Springer. [DOI:10.1007/978-3-658-33198-6_56]
113. Dehkharghanian T, Mu Y, Tizhoosh HR, Campbell CJ. Applied machine learning in hematopathology. International Journal of Laboratory Hematology. 2023;45:87-94. [DOI:10.1111/ijlh.14110]
114. Li N, Lewin A, Ning S, Waito M, Zeller MP, Tinmouth A, et al. Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine. Transfusion. 2025;65(1):22-8. https://doi.org/10.1111/trf.16741 [DOI:10.1111/trf.18077]
115. Asti G, D'Amico S, Carota L, Piscia D, Casadei F, Merleau NSC, et al. An Artificial Intelligence-Based Federated Learning Platform to Boost Precision Medicine in Rare Hematological Diseases: An Initiative By GenoMed4all and Synthema Consortia. Blood. 2024;144:4989. [DOI:10.1182/blood-2024-205541]
116. Schouten D, Nicoletti G, Dille B, Chia C, Vendittelli P, Schuurmans M, et al. Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications. Medical Image Analysis. 2025:103621. [DOI:10.1016/j.media.2025.103621]
117. Reinecke D, Maroouf N, Smith A, Alber D, Markert J, Goff NK, et al. Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning. medRxiv. 2024. [DOI:10.1101/2024.08.25.24312509]
118. Cheng FM, Lo SC, Lin CC, Lo WJ, Chien SY, Sun TH, et al. Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube. Sci Rep. 2024;14(1):8350. [DOI:10.1038/s41598-024-58580-z]
119. Salama ME, Otteson GE, Camp JJ, Seheult JN, Jevremovic D, Holmes DR, 3rd, et al. Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia. Cancers (Basel). 2022;14(10). [DOI:10.3390/cancers14102537]
120. Asar TO, Ragab M. Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning. Sci Rep. 2024;14(1):21755. [DOI:10.1038/s41598-024-72900-3]
121. Pan L, Li L, Qiu Y, Ling X, Wang C, Wu Z, et al. A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia. Medicine (Baltimore). 2024;103(20):e38205. [DOI:10.1097/MD.0000000000038205]
122. Lyons J, Desai V, Xu Y, Ridgeway G, Finkle W, Solari P, et al. Development and Validation of an Algorithm for Identifying Patients with Hemophilia A in an Administrative Claims Database. Value Health. 2018;21(9):1098-103. [DOI:10.1016/j.jval.2018.03.008]
123. Lei Y, Wei H, Gao X. High temporal resolution prediction of mortality risk for single AML patient via deep learning. iScience. 2024;27(8):110458. [DOI:10.1016/j.isci.2024.110458]
124. Didi I, Alliot JM, Dumas PY, Vergez F, Tavitian S, Largeaud L, et al. Artificial intelligence-based prediction models for acute myeloid leukemia using real-life data: A DATAML registry study. Leuk Res. 2024;136:107437. [DOI:10.1016/j.leukres.2024.107437]
125. Zhao S, Wang J, Jin C, Zhang X, Xue C, Zhou R, et al. Stacking Ensemble Learning-Based [18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma. Journal of Nuclear Medicine. 2023;64(10):1603-9. [DOI:10.2967/jnumed.122.265244]
126. Jardim LL, Schieber TA, Santana MP, Cerqueira MH, Lorenzato CS, Franco VKB, et al. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network. J Thromb Haemost. 2024;22(9):2426-37. [DOI:10.1016/j.jtha.2024.05.017]
127. Khosla E, Ramesh D. Phase classification of chronic myeloid leukemia using convolution neural networks2018. 1-6 p. [DOI:10.1109/RAIT.2018.8389068]
128. Sasaki K, Jabbour EJ, Ravandi F, Konopleva M, Borthakur G, Wierda WG, et al. The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes. Am J Hematol. 2021;96(2):241-50. [DOI:10.1002/ajh.26047]
129. Mehrbakhsh Z, Hassanzadeh R, Behnampour N, Tapak L, Zarrin Z, Khazaei S, et al. Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study. BMC Med Inform Decis Mak. 2024;24(1):261. [DOI:10.1186/s12911-024-02645-6]
130. Saleem M, Aslam W, Lali MIU, Rauf HT, Nasr EA. Predicting thalassemia using feature selection techniques: A comparative analysis. Diagnostics. 2023;13(22):3441. [DOI:10.3390/diagnostics13223441]
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 All Rights Reserved | Iranian Journal of Blood and Cancer

Designed & Developed by : Yektaweb