Application of artificial intelligence and machine learning for HIV prevention interventions
- PMID: 34762838
- PMCID: PMC9840899
- DOI: 10.1016/S2352-3018(21)00247-2
Application of artificial intelligence and machine learning for HIV prevention interventions
Abstract
In 2019, the US Government announced its goal to end the HIV epidemic within 10 years, mirroring the initiatives set forth by UNAIDS. Public health prevention interventions are a crucial part of this ambitious goal. However, numerous challenges to this goal exist, including improving HIV awareness, increasing early HIV infection detection, ensuring rapid treatment, optimising resource distribution, and providing efficient prevention services for vulnerable populations. Artificial intelligence has had a pivotal role in revolutionising health care and has shown great potential in developing effective HIV prevention intervention strategies. Although artificial intelligence has been used in a few HIV prevention intervention areas, there are challenges to address and opportunities to explore.
Copyright © 2022 Elsevier Ltd. All rights reserved.
Conflict of interest statement
Declaration of interests We declare no competing interests.
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References
-
- Heron M. Deaths: Leading Causes for 2017 2019 [Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf. - PubMed
-
- Ending the HIV Epidemic: A Plan for America 2019 [Available from: https://www.cdc.gov/endhiv/index.html.
-
- Fauci AS, Redfield RR, Sigounas G, Weahkee MD, Giroir BP. Ending the HIV Epidemic: A Plan for the United States. JAMA. 2019;321(9):844–5. - PubMed
-
- Division of HIV/AIDS Prevention Strategic Plan 2017 – 2020. July 14th, 2020. ed2017.
-
- Yurtsever E, Lambert J, Carballo A, Takeda K. A Survey of Autonomous Driving: Common Practices and Emerging Technologies. IEEE Access. 2020;8:58443–69.
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