Application of Artificial Intelligence (AI) and Machine Learning (ML) in Health Applications for HIV/AIDS Prevention: A Systematic Review

Aug 4, 2026

HIV/AIDS remains a worsening global health challenge despite prevention efforts such as health education and HIV screening. This systematic review applies PRISMA 2020 to evaluate the effectiveness of artificial intelligence and machine learning for HIV prevention applications. Searches in PubMed, ScienceDirect, and Google Scholar (2015–2024) assess study quality using the Joanna Briggs Institute checklist. Findings indicate AI/ML can strengthen prevention programs via risk detection from digital data, support for status disclosure through virtual reality, education chatbots, and analytical insights to reduce transmission and guide prevention strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Background

Artificial Intelligence and Machine Learning

Subjects and Method

Study Design

Steps of Systematic Review

Results

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Keywords

Application of Artificial Intelligence (AI) and Machine Learning (ML) in Health Applications for HIV/AIDS Prevention: A Systematic Review Aulia Dwi Yuliana1), Nurholis Majid1), Ahnav BilAuvaq1), Eksa Satya Pertiwi1), Jessica Febe Immanuela1) 1)Research and Development Department, Sinergi Sehat Indonesia, Yogyakarta. Received: 13 December 2023; Accepted: 06 January, 2024; Available online: 10 January, 2025 ABSTRACT Background: HIV/AIDS has become a global problem that continues to increase every year, despite various prevention efforts such as health education and HIV screening. To overcome this challenge, innovative strategies are needed by integrating artificial intelligence and digital technology to develop more effective HIV/AIDS prevention interventions. The aim of this study is to determine the effectiveness of using AI and ML in applications for HIV prevention and can be an evidence base for the development of Health applications for HIV Prevention. Subjects and Method: The research method used was a systematic review based on PRISMA 2020. The databases used are PubMed, Science Direct, and Google Scholar with study criteria published in 2015-2024. The keywords used are “Artificial Intelligence and Machine Learning and HIV”,“Artificial Intelligence and HIV”,“Machine Learning and HIV, ‘Artificial Intelligence and HIV’ and Systematic Review”, and “Artificial Intelligence and Machine Learning in HIV/AIDS Prevention”. The Joanna Briggs Institute (JBI) checklist was used to assess the quality of the included studies. Results: Based on the results of the review, AI and ML have proven to be effective in improving HIV/AIDS prevention programs. Benefits include the use of digital data to detect at-risk groups, virtual reality programs to help with status disclosure, chatbots for education, and data analysis to understand the causes of transmission and how to prevent it. An HIV prevention chatbot that can aid in prevention messaging, encourage self-testing, and personalized treatment strategies would be transformational in a low-resource setting. Conclusion: AI and ML approaches can be an important solution in improving the effectiveness of HIV/AIDS prevention programs, although they are still at an early stage and face various challenges. Future research should identify the potential of AI and ML to be developed and implemented more widely. Keywords: Artificial Intelligence (AI), Machine Learning (ML), HIV/AIDS Correspondence: Aulia Dwi Yuliana. Research and Development Department, Sinergi Sehat Indonesia. Sleman, Special Region of Yogyakarta, Indonesia. Email: [aulia@sinergisehatindonesia.or.id](aulia@sinergisehatindonesia.or.id). Cite this as: Yuliana AD, Majid N, Auvaq AB, Pertiwi ES, Immanuella JF (2025) . Application of Artificial Intelligence (AI) and Machine Learning (ML) in Health Applications for HIV/AIDS Prevention: A Systematic Review. Indones J Med. 10(01): 41-50. [https://doi.org/10.26911/theijmed.2025.10.01.04](https://doi.org/10.26911/theijmed.2025.10.01.04). © Aulia Dwi Yuliana. Published by Master’s Program of Public Health, Universitas Sebelas Maret, Surakarta. This open-access article is distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0). Re-use is permitted for any purpose, provided attribution is given to the author and the source is cited. BACKGROUND HIV/AIDS is a disease that is still a global problem today. It is estimated that there will be 39.9 million people living with HIV by the end of 2023 an estimated 630,000 people will die from HIV-related causes and around 1.3 million people will contract HIV (WHO, 2024a). Meanwhile, in Indonesia, the cumulative number of people living with HIV reported up to December 2023 was 407,577, while the cumulative number of AIDS cases reported up to December 2023 was 159,130 (Kemenkes RI, 2024). There is no definitive cure for HIV infection. However, with access to effective HIV prevention, diagnos

FAQs

What is the purpose of this systematic review?

To determine how effective AI and ML are when used in applications for HIV prevention, providing an evidence base for developing health applications for HIV prevention.

Which databases and time range were used for the study selection?

The review used PubMed, ScienceDirect, and Google Scholar, including studies published between 2015 and 2024.

How do AI and ML support HIV/AIDS prevention according to the findings?

They help improve prevention programs through digital data analysis to detect at-risk groups, virtual reality for status disclosure, education chatbots, and data analysis to understand transmission causes and prevention approaches.

Aug 4, 2026

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