This paper is in the following e-collection/theme issue:

Digital Health, Telehealth and e-Innovation in Clinical Settings (562) Scientometrics, Infometrics, and Altmetrics (124) Artificial Intelligence (2184) HIV/AIDS/STI Prevention and Care (1053)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64829, first published .
Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024

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

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

Authors of this article:

Musa Jaiteh1 Author Orcid Image ;   Edith Phalane1 Author Orcid Image ;   Yegnanew A Shiferaw2 Author Orcid Image ;   Lateef Babatunde Amusa3, 4 Author Orcid Image ;   Hossana Twinomurinzi3 Author Orcid Image ;   Refilwe Nancy Phaswana-Mafuya1 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Jaiteh M, Phalane E, Shiferaw Y, Jallow H, Phaswana-Mafuya R. The Application of Machine Learning Algorithms to Predict HIV Testing Using Evidence from the 2002–2017 South African Adult Population-Based Surveys: An HIV Testing Predictive Model. Tropical Medicine and Infectious Disease 2025;10(6):167 View
  2. Jaiteh M, Phalane E, Shiferaw Y, Phaswana-Mafuya R. The status of machine learning in HIV testing in South Africa: a qualitative inquiry with stakeholders in Gauteng province. Frontiers in Digital Health 2025;7 View
  3. Gong X, Yao Y, Wang L, Meng K, Li H. Global burden and forecast of infectious diseases attributable to drug use: evidence from GBD 2021. Frontiers in Public Health 2025;13 View