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.

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