The Status of Machine Learning in HIV Testing in South Africa: A Qualitative Inquiry with Stakeholders in Gauteng Province

Musa Jaiteh^1*Edith Phalane¹Yegnanew A Shiferaw²Refilwe Nancy Phaswana-Mafuya¹

• ¹South African Medical Research Council/University of Johannesburg (SAMRC/UJ) Pan African Centre for Epidemics Research (PACER) Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
• ²Department of Statistics, Faculty of Science, University of Johannesburg, Johannesburg, Gauteng, South Africa

Background: The human immunodeficiency virus (HIV) remains one of the leading causes of death globally, with South Africa bearing a significant burden. As an effective way of reducing HIV transmission, HIV testing interventions are crucial and require the involvement of key stakeholders, including healthcare professionals and policymakers. New technologies like machine learning are remarkably reshaping the healthcare landscape, especially in HIV testing. However, their implementation from the stakeholders’ point of view remains unclear. This study explored the perspectives of key stakeholders in Gauteng Province on the status of machine learning applications in HIV testing in South Africa.Methods: The study used an exploratory qualitative approach to recruit 15 stakeholders working in government and non-government institutions rendering HIV testing services. The study participants were healthcare professionals such as public health experts, lab scientists, medical doctors, nurses, HIV testing services, and retention counselors. Individual-based in-depth interviews were conducted using open-ended questions. Thematic content analysis was used, and results were presented in themes and sub-themes. Results: Three main themes were determined, namely awareness level, existing applications, and perceived potential of machine learning in HIV testing interventions. A total of nine sub-themes were discussed in the study: limited knowledge among frontline workers, research vs. implementation gap, need for education, self-testing support, data analysis tools, counseling aids, youth engagement, system efficiency, and data-driven decisions. The study shows that integration of machine learning would enhance HIV risk prediction, individualized testing through HIV self-testing (HIVST), and youth engagement. This is crucial for reducing HIV transmission, addressing stigma, and optimizing resource allocation. Despite the potential, machine learning is underutilized in HIV testing services beyond statistical analysis in South Africa. Key gaps identified were a lack of implementation of research findings and a lack of awareness among frontline workers and end-users. Conclusion: Policymakers should design educational programs to improve awareness of existing machine learning initiatives and encourage the implementation of research findings into HIV testing services. A follow-up study should assess the feasibility, structural challenges, and design implementation strategies for the integration of machine learning in HIV testing in South Africa.