Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review DOI Open Access
Bahar Senel, Hayati Beka

Cumhuriyet Tıp Dergisi/Cumhuriyet Üniversitesi Tıp Fakültesi dergisi, Год журнала: 2025, Номер 47(1), С. 10 - 21

Опубликована: Март 29, 2025

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

Язык: Английский

Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review DOI Open Access
Bahar Senel, Hayati Beka

Cumhuriyet Tıp Dergisi/Cumhuriyet Üniversitesi Tıp Fakültesi dergisi, Год журнала: 2025, Номер 47(1), С. 10 - 21

Опубликована: Март 29, 2025

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

Язык: Английский

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