Application of Artificial Intelligence (AI) and Machine Learning (ML) in Health Applications for HIV/AIDS Prevention: A Systematic Review - Aulia Dwi Yuliana; Majid, Nurholis; Auvaq, Ahnav Bil; Pertiwi, Eksa Satya; Immanuela, Jessica Febe (Indonesian Journal of Medicine, 2025)
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.     Method: The research method used was a desk review or systematic review related to artificial intelligence and machine learning in HIV prevention and PrEP use. 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”, “Artificial Intelligence and Machine Learning in HIV/AIDS Prevention”.                      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.