The use of conversational agents (chatbots) in HIV prevention is still in its infancy. Nonetheless, there has been significant investment in their development and use, including in healthcare. As evident in Table 1, chatbots have been posited as a promising tool to provide personalized health information and offer emotional support in diverse and resource-constrained contexts [6••, 7, 8•]. Notably, a systematic review of chatbot interventions in Africa found that 4 (33%) of the 12 papers meeting inclusion criteria focused on the use of chatbots in HIV prevention and care. Beyond the finding of how few papers exist on the topic in Africa, other insights from the review include infrastructure challenges (mobile phones, data costs, and electricity), the barrier of indigenous language use, a focus on user experience and design, and regulatory, ethical, and data security issues [6••]. A broader review of chatbots in healthcare reports shows that the chatbot evidence base consists primarily of descriptive, quasi-experimental, and qualitative studies. Most studies are aimed at treatment, rather than prevention, with monitoring and health care system support being prevalent. Chatbots were found to typically be delivered by SMS or app and almost always using text rather than audio, video, virtual reality, or other modalities [3••]. A third review of mHealth tools to promote HIV pre-exposure prophylaxis (PrEP) uptake and adherence found that chatbots, primarily rules-based rather than AI to date, were included among the most common mHealth interventions encouraging HIV prevention that also included gamification, medication logs, testing location maps, and notification reminders [8•].

Several chatbots, such as the pioneering “Amanda Selfie” in Brazil, have been developed with a focus on key populations. Amanda Selfie, a transgender chatbot used to stimulate PrEP interest in adolescents, can facilitate sensitive discussions on sex, STIs, and PrEP, and even identify individuals at higher risk for HIV [9]. Using the theoretical framework of Davis [10] qualitative work with men who have sex with men (MSM) in Malaysia found that the perceived usefulness of chatbots hinged around questions of whether the information provided by the chatbot was accurate, and whether or not it could provide emotional support. Ease of use was influenced by considerations of cost, convenience of access, and software bugs [11]. Similar results were obtained from focus group discussions with cis-gender female sex workers in South Africa who were found to be open to chatbots and other mHealth tools for HIV care. Additional barriers to accessing and using these tools emerge in low-income groups such as these women who note that inconsistent phone ownership and threats to the privacy of such tools dues to shared phone ownership [12]. Finally, the Nolwazi bot, an isiZulu-speaking conversational agent for HIV self-testing support, showed high acceptability among users, particularly men; around 80% of those engaging with the chatbot preferred the experience over talking with a human counsellor and 77% reported that they felt they were talking with a real person [13, 14]. A key benefit noted across many of these studies is the widely reported perception that chatbots are less “judgmental” compared to interacting with a person, making people more likely to share sensitive information with them [7]. Engaging directly with chatbots can also improve engagement in HIV testing and prevention for high-risk groups who may face barriers to receiving care at traditional primary healthcare facilities by providing access to curated information, increasing anonymity and reducing stigma [15].

It is not only users who express interest in the application of chatbots to HIV prevention and care. Recent group interviews with HIV research assessment staff, intervention coaches, and community advisory board members uncovered interest in the use of chatbots to improve capacity, consistency, convenience, and quality of services, for example, by automatically conducting check-ins, follow-ups, referral linkages, and scheduling for their clients [16]. The integration of chatbots into a diverse range of health settings allows for personalized and accessible support, including the provision of appropriate resources and treatment recommendations based on patient responses, thereby reducing staff burden. However, the current body of research is not representative of diverse geographies, cultures, and age groups, with most studies conducted in high-income countries and focusing on the use of text-based chatbots [3••, 6••]. This lack of diversity limits the generalizability of the findings.