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Breaking barriers: harnessing artificial intelligence for a stigma-free, efficient HIV prevention assessment among adults in South Africa

Article scientifique 2026 Anglais

Résumé

Background: The quality of interactions between healthcare providers (HCPs) and recipients of care (ROC) are important for assessing HIV vulnerability and determining PrEP eligibility. However, these conversations are often limited because of high HCP workload and time constraints. Conversational agents powered by large language models (LLM) offer a promising solution to support such interactions. We explored the potential of an LLM-powered app prototype "Your Choice" to engage people who have recently tested HIV negative in discussions on HIV prevention and PrEP. Objective: We assessed usability, acceptability, feasibility and appropriateness of the LLM-powered app prototype "Your Choice" in identifying HIV vulnerability and summarizing relevant information to support HCPs in designing personalised HIV prevention plans. Methods: Using a human-centred design (HCD) approach, we co-developed the "Your Choice" app. Between August 2023 and March 2024, we conducted surveys with ROCs and HCPs following app use. We also analysed app-user interactions for themes and content patterns. Quantitative data were analysed descriptively; qualitative data underwent content analysis. Results: = 16) experienced in PrEP service delivery reviewed the summaries and the interactions with ROC from the app. In the pre-evaluation survey, only one expressed concern about trusting technology (1/16; 6.3%) and two thirds preferred their own judgement for ROC care (11/16; 68.8%). HCPs rated the app with a high usability scale score (SUS 78/100, indicating good usability). The app fostered open, stigma-free discussions on sex, sexuality, and HIV prevention. Conclusion: An LLM-powered conversational agent like "Your Choice" shows promise for private, stigma-free HIV prevention support, including PrEP uptake, supporting the decision-making around PrEP initiation. It can also help providers deliver more targeted care. Future research should address technological trust and clinical integration.

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Govathson, C., Chetty-Makkan, C., Greener, R., Frade, S., Rech, D., Morris, S., Richard, Y., Mendonca, R., Maricich, N., Long, L., Pascoe, S. (2026). Breaking barriers: harnessing artificial intelligence for a stigma-free, efficient HIV prevention assessment among adults in South Africa. https://doi.org/10.3389/fdgth.2025.1731002

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Auteur(s)

Caroline Govathson Candice Chetty-Makkan Ross Greener Sasha Frade Dino Rech Sarah Morris Yohann Richard Rouella Mendonca Natalie Maricich Lawrence Long Sophie Pascoe

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