Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

Bahar Senel; Hayati Beka

doi:10.7197/cmj.1630164

EN TR

Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

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

HIV Tanı ve Tedavisinde Yapay Zeka: Kapsamlı Bir Derleme

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

Kaynakça

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