Evidence map›Paper›PMID 41527742›Full record

ArticleTransactions of the Royal Society of Tropical Medicine and Hygiene2026

A qualitative study assessing the acceptability of a multi-agent AI Chatbot for providing HIV and mental health support among men who have sex with men and transgender women in KwaZulu-Natal, South Africa.

Hilton Humphries, Lindani Msimango, Zimasa Tshawe, Natasha Gcelu, Kurt Ferreira, Jacqueline Pienaar, Elise M van der Elst, Danielle Giovenco, Don Operario, Eduard J Sanders and 1 more

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Article in Transactions of the Royal Society of Tropical Medicine and Hygiene, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hilton HumphriesCentre for Community-Based Research, Human Science Research Council, Sweetwaters 3201, South Africa.ORCID 0000-0001-8503-4127
Lindani MsimangoCentre for Community-Based Research, Human Science Research Council, Sweetwaters 3201, South Africa.
Zimasa TshaweCentre for Community-Based Research, Human Science Research Council, Sweetwaters 3201, South Africa.
Natasha GceluCentre for Community-Based Research, Human Science Research Council, Sweetwaters 3201, South Africa.
Kurt FerreiraWorien, Hilton 3245, South Africa.
Jacqueline PienaarAurum Institute, Johannesburg 2193, South Africa.
Elise M van der ElstFaculty of Social and Behavioural Sciences, University Utrecht, Utrecht 3508, The Netherlands.
Danielle GiovencoHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Don OperarioDepartment of Behavioral, Social, and Health Education Sciences, Emory University, Atlanta, GA 30322, USA.
Eduard J SandersAurum Institute, Johannesburg 2193, South Africa.
Alastair van HeerdenSyndicate for Public Science and Emerging Technologies, Wits Health Consortium, Johannesburg 2193, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTransgender women (TGW) and men who have sex with men (MSM) are disproportionately affected by human immunodeficiency virus (HIV) and mental health challenges. Mental well-being influences uptake and adherence to HIV prevention and treatment. However, gaps in mental health service delivery present challenges for scalability in public health systems. Artificial intelligence (AI)-driven chatbots may offer a novel, scalable solution to expand access to mental health support.

methodsThis qualitative study was conducted at the Aurum POP INN clinic in Pietermaritzburg, KwaZulu-Natal. A multi-agent AI chatbot, designed to simulate supportive counselling based on the Inuka model, was piloted with TGW and MSM. Ten participants engaged in in-depth interviews after interacting with the chatbot. An additional 34 participants experienced both chatbot and in-person counselling through a randomised crossover design and then participated in four focus group discussions. The Unified Theory of Acceptance and Use of Technology and the Acceptability of Healthcare Interventions Framework guided the analysis.

resultsThe chatbot was generally acceptable, with participants valuing its privacy, convenience and human-like interaction. Acceptability was enhanced by associations with modernity and anonymity. Trust, usability and accessibility improved engagement. Key barriers included slow response times, limited rapport and repetitive messaging.

conclusionsAI chatbots offer a promising, scalable approach to supporting mental health among key populations in HIV care.

Indexed as

Artificial IntelligenceCounselingHIV InfectionsHomosexuality, MalePatient Acceptance of Health CareTransgender PersonsAdultFemaleFocus GroupsHumansMaleMental HealthMental Health TeletherapyQualitative ResearchSouth AfricaYoung Adultartificial intelligencechatbotsHIV preventionHIV treatmentkey populationsmental healthMSMSouth Africatransgender women

Identifiers

PMID41527742
PMCPMC12863077

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.