Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

Bahar Senel; Hayati Beka

doi:10.7197/cmj.1630164

Review

HIV Tanı ve Tedavisinde Yapay Zeka: Kapsamlı Bir Derleme

Year 2025, Volume: 47 Issue: 1, 10 - 21, 29.03.2025

Bahar Senel , Hayati Beka

Abstract

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.

Keywords

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

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Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

Year 2025, Volume: 47 Issue: 1, 10 - 21, 29.03.2025

Bahar Senel , Hayati Beka

Abstract

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.

Keywords

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

References

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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
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Details

Primary Language	English
Subjects	Digital Health, Health Systems
Journal Section	Meta-analysis and Systematic Review
Authors	Bahar Senel 0000-0002-9175-6107 Hayati Beka 0000-0002-5509-0248
Early Pub Date	March 29, 2025
Publication Date	March 29, 2025
Submission Date	January 31, 2025
Acceptance Date	March 18, 2025
Published in Issue	Year 2025Volume: 47 Issue: 1

Cite

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

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