Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

Bahar Senel; Hayati Beka

doi:10.7197/cmj.1630164

Derleme

HIV Tanı ve Tedavisinde Yapay Zeka: Kapsamlı Bir Derleme

Yıl 2025, Cilt: 47 Sayı: 1, 10 - 21, 29.03.2025

Bahar Senel , Hayati Beka

https://doi.org/10.7197/cmj.1630164

Öz

Amaç: Bu derleme, Yapay Zeka'nın (YZ) HIV tanısı, tedavi optimizasyonu ve epidemiyolojik modelleme alanlarındaki uygulamalarını incelemektedir. YZ'nin erken tanılamayı nasıl iyileştirdiği, antiretroviral tedavi (ART) regimlerini nasıl kişiselleştirdiği ve halk sağlığı stratejilerine nasıl destek sağladığı ele alınmakta, ayrıca etik ve erişilebilirlik zorlukları tartışılmaktadır.

Yöntem: 2010-2024 yılları arasında yayımlanan hakemli çalışmaları incelemek için PubMed, Scopus ve Web of Science veritabanlarında sistematik bir literatür taraması yapılmıştır. Ayrıca, Dünya Sağlık Örgütü (WHO) ve UNAIDS gibi ilgili politika belgeleri de gözden geçirilmiştir. HIV tanısı, tedavisi ve epidemiyolojisinde YZ uygulamaları üzerine yapılan çalışmalar dahil edilmiştir, hakemli olmayan, İngilizce olmayan ve ilgisiz çalışmalar hariç tutulmuştur. Seçilen çalışmalar, ana tematik alanlara göre kategorize edilmiştir.

Bulgular: YZ, erken tanılamada makine öğrenimi modelleri ile doğruluğu artırarak HIV tanısının iyileştirilmesinde önemli ilerlemeler kaydetmiştir. Tedavi alanında, YZ destekli modeller ART rejimlerini optimize etmekte ve ilaç direnç desenlerini tahmin etmektedir. Epidemiyolojik modelleme, YZ'nin büyük veri setlerini analiz etme yeteneği sayesinde hedeflenmiş müdahalelere rehberlik etmektedir. Ancak, algoritma önyargıları, veri gizliliği endişeleri ve düşük kaynaklı bölgelerde YZ'nin sınırlı kabulü gibi zorluklar uygulanabilirlik açısından engel oluşturmaktadır.

Sonuç: YZ, HIV yönetimini tanı, tedavi ve salgın kontrolü açısından dönüştürmüştür. Gelecek araştırmalar, YZ modellerini iyileştirmeye, veri kapsayıcılığını artırmaya ve YZ'nin küresel sağlık sistemlerine etik ve eşitlikçi entegrasyonunu sağlama konusunda odaklanmalıdır, böylece etki maksimize edilebilir.

Anahtar Kelimeler

Yapay Zeka, HIV Tanı ve Tedavisi, Makine Öğrenimi, Epidemiyolojik Modelleme, Antiretroviral Tedavi (ART)

Kaynakça

1. UNAIDS. Global HIV & AIDS Statistics – 2023 Fact Sheet. Accessed March 14, 2025. https://www.unaids.org/en/ resources/fact-sheet
2. World Health Organization. Progress Report on HIV, Viral Hepatitis, and Sexually Transmitted Infections, 2023. Accessed March 14, 2025. https://www.who.int/ publications
3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539
4. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-29. doi:10.1038/s41591-018-0316-z
5. Bekker LG, Alleyne G, Baral S, et al. Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society—Lancet Commission. Lancet. 2018;392(10144):312-358. doi:10.1016/S0140-6736(18)31070-5
6. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
7. Jaiteh M, Phalane E, Shiferaw YA, Voet KA, Phaswana-Mafuya RN. Utilization of machine learning algorithms for the strengthening of HIV testing: a systematic review. Algorithms. 2024;17(8):362. doi:10.3390/a17080362
8. Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82-97. doi:10.1109/MSP.2012.2205597
9. Zhang Z, Beck MW, Winkler DA, et al. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann Transl Med. 2018;6(11):216. doi:10.21037/atm.2018.05.32
10. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-1930. doi:10.1161/CIRCULATIONAHA.115.001593
11. Turbé V, Herbst C, Mngomezulu T, et al. Deep learning of HIV field-based rapid tests. Nat Med. 2021;27:1165–1170. doi:10.1038/s41591-021-01384-9
12. Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149-153. doi:10.1093/cid/cix731
13. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/ science.aax2342
14. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e194. doi:10.7861/fhj.2021-0095
15. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature21056
16. Owens DK, Holodniy M, Garber AM, et al. Polymerase chain reaction for the diagnosis of HIV infection in adults. A meta-analysis with recommendations for clinical practice and study design. Ann Intern Med. 1996;124(9):803-815. doi:10.7326/0003-4819-124-9-199605010-00004
17. Wu L, Xia D, Xu K. Multi-clinical factors combined with an artificial intelligence algorithm diagnosis model for HIV-infected people with bloodstream infection. Infect Drug Resist. 2023;16:6085-6097. doi:10.2147/IDR.S423709.- bakteriyemi ile ilgili
18. Turbé V, Herbst C, Mngomezulu T, et al. Deep learning of HIV field-based rapid tests. Nat Med. 2021;27:1165–1170. doi:10.1038/s41591-021-01384-9
19. Latt PM, Soe NN, Xu X, et al. Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence-Based Risk Assessment Tool. Open Forum Infect Dis. 2024;11(3):ofae011. Published 2024 Jan 8. doi:10.1093/ofid/ofae011
20. Alexander TS. Human Immunodeficiency Virus Diagnostic Testing: 30 Years of Evolution. Clin Vaccine Immunol. 2016;23(4):249-253. Published 2016 Apr 4. doi:10.1128/CVI.00053-16
21. Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater. 2019;18(5):435-441. doi:10.1038/s41563-019-0338-z.
22. Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16-26. doi:10.1055/s-0039-1677908.
23. Ávila-Ríos S, Parkin N, Swanstrom R, et al. Next-generation sequencing for HIV drug resistance testing: laboratory, clinical, and implementation considerations. Viruses. 2020;12(6):617. doi:10.3390/v12060617
24. Multicenter AIDS Cohort Study (MACS) dataset. Available from: https://www.niaid.nih.gov/research/multicenter-aids -cohort-study-public-data-set
25. Stanford HIV Drug Resistance Database (HIVdb). Erişim tarihi: 15 Mart 2025. https://hivdb.stanford.edu/.
26. EKMUD. Publicly available chest X-ray datasets for HIV-related imaging studies. Erişim tarihi: 15 Mart 2025. https://www.ekmud.org.tr/sunum/indir/1968-hiv-ve-akciger-firsatci-enfeksiyonlari.
27. Barçın Öztürk Ş. HIV ile Yaşayan Bireylerde Kanser: Kanser Taramaları. HIV/AIDS ve Komorbiditeler: Ege Bölgesi Sempozyumu. 13 Ekim 2023. KLİMİK.
28. Seboka BT, Yehualashet DE, Tesfa GA. Artificial intelligence and machine learning-based prediction of viral load and CD4 status of people living with HIV (PLWH) on anti-retroviral treatment in Gedeo Zone public hospitals. Int J Gen Med. 2023;16:435-451. doi:10.2147/IJGM.S397031.
29. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and prospects. Drug Discov Today. 2019;24(3):773-780. doi:10.1016/j.drudis.2018.11. 014
30. Jin R, Zhang L. AI applications in HIV research: advances and future directions. Front Microbiol. 2025;16. doi:10.3389/ fmicb.2025.1541942.
31. Noguera-Julian M, Edgil D, Harrigan PR, et al. Next-generation human immunodeficiency virus sequencing for patient management and drug resistance surveillance. J Infect Dis. 2017;216(Suppl 9):S829–S833. doi:10.1093/ infdis/jix397.
32. Fogel JM, Bonsall D, Cummings V, et al. Performance of a high-throughput next-generation sequencing method for analysis of HIV drug resistance and viral load. J Antimicrob Chemother. 2020;75(12):3510–3516. doi:10.1093/jac/ dkaa352
33. Vashisht A, Mondal AK, Vashisht V, et al. Enhancing precision in HIV treatment: validation of a robust next-generation sequencing system for drug resistance mutation analysis. Diagnostics. 2024;14(16):1766. doi:10.3390/diagnostics 14161766.
34. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219. doi:10.1056/NEJMp1606181
35. McRae MP, Rajsri KS, Alcorn TM, et al. Smart diagnostics: combining artificial intelligence and in vitro diagnostics. Sensors. 2022;22(17):6355. doi:10.3390/s22176355
36. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for the detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410.
37. Lipton ZC. The mythos of model interpretability. Queue. 2016;16(3):31–57.
38. Zhao L, Wang J, Pang L, Liu Y, Zhang J. GANsDTA: Predicting drug-target binding affinity using GANs. Front Genet. 2020;10. doi:10.3389/fgene.2019.01243.
39. Wang Y, Bryant SH, Cheng T, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res. 2017;45(D1):D955–D963.
40. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589.
41. Bertoline LM, Lima AN, Krieger JE, Teixeira SK. Before and after AlphaFold2: An overview of protein structure prediction. Front Bioinform. 2023;3:1120370. doi:10.3389/ fbinf.2023.1120370.
42. Vemula D, Jayasurya P, Sushmitha V, Kumar YN, Bhandari V. CADD, AI and ML in drug discovery: A comprehensive review. Eur J Pharm Sci. 2023 Feb 1;181:106324. doi:10. 1016/j.ejps.2022.106324.
43. Nishan MDNH. AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world. J Glob Health. 2025 Jan 10;15:03002. doi:10.7189/jogh.15.03002
44. Ambreen S, Umar M, Noor A, Jain H, Ali R. Advanced AI and ML frameworks for transforming drug discovery and optimization: with innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine. Eur J Med Chem. 2025;284:117164. doi:10.1016/ j.ejmech.2024.117164.
45. Kanakia A, Sale M, Zhao L, Zhou Z. AI in action: redefining drug discovery and development. Clin Transl Sci. 2025;18(2):e70149. doi:10.1111/cts.70149.
46. Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform. 2016;35(1):3-14.
47. Revell AD, Wang D, Perez-Elias MJ, et al. 2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings. J Antimicrob Chemother. 2021;76(7):1898-1906. doi:10.1093/jac/dkab 078.
48. Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507-2509.
49. Murdoch WJ, Singh C, Kumbier K, et al. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci U S A. 2019;116(44):22071-22080.
50. Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine. Nat Med. 2022;28(1):31-38.
51. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22(5):1589-1604.
52. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-731.
53. Shaik T, Tao X, Higgins N, et al. Remote patient monitoring using artificial intelligence: current state, applications, and challenges. arXiv preprint arXiv:2301.10009. 2023
54. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-76.
55. Sangeeta. Pharmacogenomics: personalized medicine and drug response prediction. Pharma Innovation. 2019;8(1):845-848. doi:10.22271/tpi.2019.v8.i1n.25487.
56. Krittanawong C, Zhang H, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657-2664.
57. Perelson AS, Ribeiro RM. Modeling the within-host dynamics of HIV infection. BMC Biol. 2013;11(1):96. doi:10.1186/1741-7007-11-96.
58. Hill AL, Rosenbloom DIS, Fu F, et al. Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1. Proc Natl Acad Sci U S A. 2014;111(37):13475-13480. doi:10.1073/pnas.1406663111.
59. Mittelstadt BD, Allo P, Taddeo M, et al. The ethics of algorithms: mapping the debate. Big Data Soc. 2016;3(2):205395171667967
60. Steiner MC, Gibson KM, Crandall KA. Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data. Viruses. 2020;12(5):560. Published 2020 May 19. doi:10.3390/v12050560
61. Pandi MT, Koromina M, Tsafaridis I, et al. A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants. Hum Genomics. 2021;15(1):51. doi:10.1186/s40246-021-00352-1.
62. Piot P, Abdool Karim SS, Hecht R, et al. Defeating AIDS—advancing global health. Lancet. 2015;386(9989):171-218.
63. Xu X, Ge Z, Chow EPF, et al. A machine-learning-based risk-prediction tool for HIV and sexually transmitted infections acquisition over the next 12 months. J Clin Med. 2022;11(7):1818. doi:10.3390/jcm11071818.
64. Kupperman MD, Leitner T, Ke R. A deep learning approach to real-time HIV outbreak detection using genetic data. PLoS Comput Biol. 2022;18(10):e1010598. doi:10.1371/ journal.pcbi.1010598
65. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579-1586. doi:10.1016/S0140-6736(20)30226-9.
66. Sun X, Nishiura H, Xiao Y. Modeling methods for estimating HIV incidence: a mathematical review. Theor Biol Med Model. 2020;17(1):1. Published 2020 Jan 22. doi:10.1186/s12976-019-0118-0
67. Kuo NI-H, Garcia F, Sönnerborg A, Zazzi M, Böhm M, Kaiser R, Polizzotto M, Jorm L, Barbieri S. Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV. arXiv. 2023. Available from: https://arxiv.org/abs/2208.08655.
68. Ghebreyesus TA. Health as the pulse of the new urban agenda. Lancet. 2016;388(10062):749-750.
69. Leslie D. Understanding artificial intelligence ethics and safety: a guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute. 2019. doi:10.5281/zenodo.3240529.
70. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745-e750. doi:10.1016/S2589-7500(21)00208-9.
71. Cabitza F, Campagner A, Balsano C. Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters. Ann Transl Med. 2020;8(7):501. doi:10.21037/atm.2020.03.63.
72. Garett R, Kim S, Young SD. Ethical Considerations for Artificial Intelligence Applications for HIV. AI. 2024; 5(2):594-601. https://doi.org/10.3390/ai5020031
73. Karabacak M, Ozkara BB, Margetis K, Wintermark M, Bisdas S. The advent of generative language models in medical education. JMIR Med Educ. 2023;9:e48163. doi:10.2196/48163.
74. Badur E. Yapay zeka sistemleri kullanılarak yapılan işleme faaliyetlerinde kişisel verilerin korunması. SÜHFD. 2024;32(4):2525-2560.
75. Damar M. Sağlık sektöründe karar destek araçları: iş zekâsı, makine öğrenmesi, derin öğrenme ve yapay zeka uygulamaları. İzmir Sosyal Bilimler Dergisi. 2024;6(2):90-115. doi:10.47899/ijss.1591168.
76. Yorgancıoğlu Tarcan G, Yalçın Balcıki P, Sebik NB. Artificial intelligence in healthcare in Türkiye and the world. Lokman Hekim J. 2024;14(1):50-60.

Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

Yıl 2025, Cilt: 47 Sayı: 1, 10 - 21, 29.03.2025

Bahar Senel , Hayati Beka

https://doi.org/10.7197/cmj.1630164

Öz

Objective: This review examines the applications of Artificial Intelligence (AI) in HIV diagnosis, treatment optimization, and epidemiological modeling. It explores how AI enhances early detection, personalizes antiretroviral therapy (ART), and supports public health strategies while addressing ethical and accessibility challenges.
Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science for peer-reviewed studies published between 2010 and 2024. Relevant policy documents from WHO and UNAIDS were also reviewed. Studies on AI applications in HIV diagnosis, treatment, and epidemiology were included, while non-peer-reviewed, non-English, and unrelated studies were excluded. Selected studies were categorized into key thematic areas.
Results: AI has significantly improved HIV diagnosis by enhancing accuracy in early detection through machine learning models. In treatment, AI-driven models assist in optimizing ART regimens and predicting drug resistance patterns. Epidemiological modeling has benefited from AI's ability to analyze large datasets, informing targeted interventions. However, challenges such as algorithmic biases, data privacy concerns, and limited AI adoption in low-resource settings remain barriers to implementation.
Conclusion: AI has transformed HIV management by improving diagnosis, treatment, and epidemic control. Future research should focus on refining AI models, increasing data inclusivity, and ensuring ethical and equitable AI integration into global healthcare systems to maximize its impact.

Anahtar Kelimeler

Artificial Intelligence, HIV Diagnosis and Treatment, Machine Learning, Epidemiological Modeling, Antiretroviral Therapy (ART).

Kaynakça

1. UNAIDS. Global HIV & AIDS Statistics – 2023 Fact Sheet. Accessed March 14, 2025. https://www.unaids.org/en/ resources/fact-sheet
2. World Health Organization. Progress Report on HIV, Viral Hepatitis, and Sexually Transmitted Infections, 2023. Accessed March 14, 2025. https://www.who.int/ publications
3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539
4. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-29. doi:10.1038/s41591-018-0316-z
5. Bekker LG, Alleyne G, Baral S, et al. Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society—Lancet Commission. Lancet. 2018;392(10144):312-358. doi:10.1016/S0140-6736(18)31070-5
6. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
7. Jaiteh M, Phalane E, Shiferaw YA, Voet KA, Phaswana-Mafuya RN. Utilization of machine learning algorithms for the strengthening of HIV testing: a systematic review. Algorithms. 2024;17(8):362. doi:10.3390/a17080362
8. Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82-97. doi:10.1109/MSP.2012.2205597
9. Zhang Z, Beck MW, Winkler DA, et al. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann Transl Med. 2018;6(11):216. doi:10.21037/atm.2018.05.32
10. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-1930. doi:10.1161/CIRCULATIONAHA.115.001593
11. Turbé V, Herbst C, Mngomezulu T, et al. Deep learning of HIV field-based rapid tests. Nat Med. 2021;27:1165–1170. doi:10.1038/s41591-021-01384-9
12. Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149-153. doi:10.1093/cid/cix731
13. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/ science.aax2342
14. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e194. doi:10.7861/fhj.2021-0095
15. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature21056
16. Owens DK, Holodniy M, Garber AM, et al. Polymerase chain reaction for the diagnosis of HIV infection in adults. A meta-analysis with recommendations for clinical practice and study design. Ann Intern Med. 1996;124(9):803-815. doi:10.7326/0003-4819-124-9-199605010-00004
17. Wu L, Xia D, Xu K. Multi-clinical factors combined with an artificial intelligence algorithm diagnosis model for HIV-infected people with bloodstream infection. Infect Drug Resist. 2023;16:6085-6097. doi:10.2147/IDR.S423709.- bakteriyemi ile ilgili
18. Turbé V, Herbst C, Mngomezulu T, et al. Deep learning of HIV field-based rapid tests. Nat Med. 2021;27:1165–1170. doi:10.1038/s41591-021-01384-9
19. Latt PM, Soe NN, Xu X, et al. Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence-Based Risk Assessment Tool. Open Forum Infect Dis. 2024;11(3):ofae011. Published 2024 Jan 8. doi:10.1093/ofid/ofae011
20. Alexander TS. Human Immunodeficiency Virus Diagnostic Testing: 30 Years of Evolution. Clin Vaccine Immunol. 2016;23(4):249-253. Published 2016 Apr 4. doi:10.1128/CVI.00053-16
21. Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater. 2019;18(5):435-441. doi:10.1038/s41563-019-0338-z.
22. Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16-26. doi:10.1055/s-0039-1677908.
23. Ávila-Ríos S, Parkin N, Swanstrom R, et al. Next-generation sequencing for HIV drug resistance testing: laboratory, clinical, and implementation considerations. Viruses. 2020;12(6):617. doi:10.3390/v12060617
24. Multicenter AIDS Cohort Study (MACS) dataset. Available from: https://www.niaid.nih.gov/research/multicenter-aids -cohort-study-public-data-set
25. Stanford HIV Drug Resistance Database (HIVdb). Erişim tarihi: 15 Mart 2025. https://hivdb.stanford.edu/.
26. EKMUD. Publicly available chest X-ray datasets for HIV-related imaging studies. Erişim tarihi: 15 Mart 2025. https://www.ekmud.org.tr/sunum/indir/1968-hiv-ve-akciger-firsatci-enfeksiyonlari.
27. Barçın Öztürk Ş. HIV ile Yaşayan Bireylerde Kanser: Kanser Taramaları. HIV/AIDS ve Komorbiditeler: Ege Bölgesi Sempozyumu. 13 Ekim 2023. KLİMİK.
28. Seboka BT, Yehualashet DE, Tesfa GA. Artificial intelligence and machine learning-based prediction of viral load and CD4 status of people living with HIV (PLWH) on anti-retroviral treatment in Gedeo Zone public hospitals. Int J Gen Med. 2023;16:435-451. doi:10.2147/IJGM.S397031.
29. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and prospects. Drug Discov Today. 2019;24(3):773-780. doi:10.1016/j.drudis.2018.11. 014
30. Jin R, Zhang L. AI applications in HIV research: advances and future directions. Front Microbiol. 2025;16. doi:10.3389/ fmicb.2025.1541942.
31. Noguera-Julian M, Edgil D, Harrigan PR, et al. Next-generation human immunodeficiency virus sequencing for patient management and drug resistance surveillance. J Infect Dis. 2017;216(Suppl 9):S829–S833. doi:10.1093/ infdis/jix397.
32. Fogel JM, Bonsall D, Cummings V, et al. Performance of a high-throughput next-generation sequencing method for analysis of HIV drug resistance and viral load. J Antimicrob Chemother. 2020;75(12):3510–3516. doi:10.1093/jac/ dkaa352
33. Vashisht A, Mondal AK, Vashisht V, et al. Enhancing precision in HIV treatment: validation of a robust next-generation sequencing system for drug resistance mutation analysis. Diagnostics. 2024;14(16):1766. doi:10.3390/diagnostics 14161766.
34. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219. doi:10.1056/NEJMp1606181
35. McRae MP, Rajsri KS, Alcorn TM, et al. Smart diagnostics: combining artificial intelligence and in vitro diagnostics. Sensors. 2022;22(17):6355. doi:10.3390/s22176355
36. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for the detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410.
37. Lipton ZC. The mythos of model interpretability. Queue. 2016;16(3):31–57.
38. Zhao L, Wang J, Pang L, Liu Y, Zhang J. GANsDTA: Predicting drug-target binding affinity using GANs. Front Genet. 2020;10. doi:10.3389/fgene.2019.01243.
39. Wang Y, Bryant SH, Cheng T, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res. 2017;45(D1):D955–D963.
40. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589.
41. Bertoline LM, Lima AN, Krieger JE, Teixeira SK. Before and after AlphaFold2: An overview of protein structure prediction. Front Bioinform. 2023;3:1120370. doi:10.3389/ fbinf.2023.1120370.
42. Vemula D, Jayasurya P, Sushmitha V, Kumar YN, Bhandari V. CADD, AI and ML in drug discovery: A comprehensive review. Eur J Pharm Sci. 2023 Feb 1;181:106324. doi:10. 1016/j.ejps.2022.106324.
43. Nishan MDNH. AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world. J Glob Health. 2025 Jan 10;15:03002. doi:10.7189/jogh.15.03002
44. Ambreen S, Umar M, Noor A, Jain H, Ali R. Advanced AI and ML frameworks for transforming drug discovery and optimization: with innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine. Eur J Med Chem. 2025;284:117164. doi:10.1016/ j.ejmech.2024.117164.
45. Kanakia A, Sale M, Zhao L, Zhou Z. AI in action: redefining drug discovery and development. Clin Transl Sci. 2025;18(2):e70149. doi:10.1111/cts.70149.
46. Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform. 2016;35(1):3-14.
47. Revell AD, Wang D, Perez-Elias MJ, et al. 2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings. J Antimicrob Chemother. 2021;76(7):1898-1906. doi:10.1093/jac/dkab 078.
48. Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507-2509.
49. Murdoch WJ, Singh C, Kumbier K, et al. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci U S A. 2019;116(44):22071-22080.
50. Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine. Nat Med. 2022;28(1):31-38.
51. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22(5):1589-1604.
52. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-731.
53. Shaik T, Tao X, Higgins N, et al. Remote patient monitoring using artificial intelligence: current state, applications, and challenges. arXiv preprint arXiv:2301.10009. 2023
54. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-76.
55. Sangeeta. Pharmacogenomics: personalized medicine and drug response prediction. Pharma Innovation. 2019;8(1):845-848. doi:10.22271/tpi.2019.v8.i1n.25487.
56. Krittanawong C, Zhang H, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657-2664.
57. Perelson AS, Ribeiro RM. Modeling the within-host dynamics of HIV infection. BMC Biol. 2013;11(1):96. doi:10.1186/1741-7007-11-96.
58. Hill AL, Rosenbloom DIS, Fu F, et al. Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1. Proc Natl Acad Sci U S A. 2014;111(37):13475-13480. doi:10.1073/pnas.1406663111.
59. Mittelstadt BD, Allo P, Taddeo M, et al. The ethics of algorithms: mapping the debate. Big Data Soc. 2016;3(2):205395171667967
60. Steiner MC, Gibson KM, Crandall KA. Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data. Viruses. 2020;12(5):560. Published 2020 May 19. doi:10.3390/v12050560
61. Pandi MT, Koromina M, Tsafaridis I, et al. A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants. Hum Genomics. 2021;15(1):51. doi:10.1186/s40246-021-00352-1.
62. Piot P, Abdool Karim SS, Hecht R, et al. Defeating AIDS—advancing global health. Lancet. 2015;386(9989):171-218.
63. Xu X, Ge Z, Chow EPF, et al. A machine-learning-based risk-prediction tool for HIV and sexually transmitted infections acquisition over the next 12 months. J Clin Med. 2022;11(7):1818. doi:10.3390/jcm11071818.
64. Kupperman MD, Leitner T, Ke R. A deep learning approach to real-time HIV outbreak detection using genetic data. PLoS Comput Biol. 2022;18(10):e1010598. doi:10.1371/ journal.pcbi.1010598
65. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579-1586. doi:10.1016/S0140-6736(20)30226-9.
66. Sun X, Nishiura H, Xiao Y. Modeling methods for estimating HIV incidence: a mathematical review. Theor Biol Med Model. 2020;17(1):1. Published 2020 Jan 22. doi:10.1186/s12976-019-0118-0
67. Kuo NI-H, Garcia F, Sönnerborg A, Zazzi M, Böhm M, Kaiser R, Polizzotto M, Jorm L, Barbieri S. Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV. arXiv. 2023. Available from: https://arxiv.org/abs/2208.08655.
68. Ghebreyesus TA. Health as the pulse of the new urban agenda. Lancet. 2016;388(10062):749-750.
69. Leslie D. Understanding artificial intelligence ethics and safety: a guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute. 2019. doi:10.5281/zenodo.3240529.
70. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745-e750. doi:10.1016/S2589-7500(21)00208-9.
71. Cabitza F, Campagner A, Balsano C. Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters. Ann Transl Med. 2020;8(7):501. doi:10.21037/atm.2020.03.63.
72. Garett R, Kim S, Young SD. Ethical Considerations for Artificial Intelligence Applications for HIV. AI. 2024; 5(2):594-601. https://doi.org/10.3390/ai5020031
73. Karabacak M, Ozkara BB, Margetis K, Wintermark M, Bisdas S. The advent of generative language models in medical education. JMIR Med Educ. 2023;9:e48163. doi:10.2196/48163.
74. Badur E. Yapay zeka sistemleri kullanılarak yapılan işleme faaliyetlerinde kişisel verilerin korunması. SÜHFD. 2024;32(4):2525-2560.
75. Damar M. Sağlık sektöründe karar destek araçları: iş zekâsı, makine öğrenmesi, derin öğrenme ve yapay zeka uygulamaları. İzmir Sosyal Bilimler Dergisi. 2024;6(2):90-115. doi:10.47899/ijss.1591168.
76. Yorgancıoğlu Tarcan G, Yalçın Balcıki P, Sebik NB. Artificial intelligence in healthcare in Türkiye and the world. Lokman Hekim J. 2024;14(1):50-60.

Toplam 76 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Dijital Sağlık, Sağlık Sistemleri
Bölüm	Metaanaliz ve Sistematik Derleme
Yazarlar	Bahar Senel 0000-0002-9175-6107 Hayati Beka 0000-0002-5509-0248
Erken Görünüm Tarihi	29 Mart 2025
Yayımlanma Tarihi	29 Mart 2025
Gönderilme Tarihi	31 Ocak 2025
Kabul Tarihi	18 Mart 2025
Yayımlandığı Sayı	Yıl 2025Cilt: 47 Sayı: 1

Kaynak Göster

AMA	Senel B, Beka H. Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review. CMJ. Mart 2025;47(1):10-21. doi:10.7197/cmj.1630164

Kapak Resmi İndir

Makale Dosyaları

Tam Metin