Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024.

Journal: Interactive journal of medical research
Published Date: May 22, 2025

Abstract

BACKGROUND: The global targets for HIV testing for achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets are still short. Identifying gaps and opportunities for HIV testing uptake is crucial in fast-tracking the second (initiate people living with HIV on antiretroviral therapy) and third (viral suppression) UNAIDS goals. Machine learning and health technologies can precisely predict high-risk individuals and facilitate more effective and efficient HIV testing methods. Despite this advancement, there exists a research gap regarding the extent to which such technologies are integrated into HIV testing strategies worldwide.

Authors

  • Musa Jaiteh
    South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
  • Edith Phalane
    South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
  • Yegnanew A Shiferaw
    Department of Statistics, Faculty of Science, University of Johannesburg, Johannesburg, South Africa.
  • Lateef Babatunde Amusa
    Center of Applied Data Science, University of Johannesburg, Johannesburg, South Africa.
  • Hossana Twinomurinzi
    Center of Applied Data Science, University of Johannesburg, Johannesburg, South Africa.
  • Refilwe Nancy Phaswana-Mafuya
    South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.

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