Reimagining HIV prevention with artificial intelligence.

We review applications of artificial intelligence (AI), including machine learning (ML), in the field of HIV prevention. ML approaches have been used to identify potential candidates for preexposure prophylaxis (PrEP) in healthcare settings in the USA and Denmark and in a population-based research setting in Eastern Africa. Although still in the proof-of-concept stage, other applications include ML with smartphone-collected and social media data to promote real-time HIV risk reduction, virtual reality tools to facilitate HIV serodisclosure, and chatbots for HIV education. ML has also been used for causal inference in HIV prevention studies. ML has strong potential to improve delivery of PrEP, with this approach moving from development to implementation. Development and evaluation of AI and ML strategies for HIV prevention may benefit from an implementation science approach, including qualitative assessments with end users, and should be developed and evaluated with attention to equity.

Application of artificial intelligence and machine learning for HIV prevention interventions.

BackgroundThe HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts. We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability.ObjectiveThis study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM.MethodsWe conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence.ResultsMost participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM.ConclusionsThe current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP. To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV.

With the advancement of artificial intelligence(AI), platforms like ChatGPT have gained traction in different fields, including Medicine. This study aims to evaluate the potential of ChatGPT in addressing questions related to HIV prevention and to assess its accuracy, completeness, and inclusivity. A team consisting of 15 physicians, six members from HIV communities, and three experts in gender and queer studies designed an assessment of ChatGPT. Queries were categorized into five thematic groups: general HIV information, behaviors increasing HIV acquisition risk, HIV and pregnancy, HIV testing, and the prophylaxis use. A team of medical doctors was in charge of developing questions to be submitted to ChatGPT. The other members critically assessed the generated responses regarding level of expertise, accuracy, completeness, and inclusivity. The median accuracy score was 5.5 out of 6, with 88.4% of responses achieving a score ≥ 5. Completeness had a median of 3 out of 3, while the median for inclusivity was 2 out of 3. Some thematic groups, like behaviors associated with HIV transmission and prophylaxis, exhibited higher accuracy, indicating variable performance across different topics. Issues of inclusivity were identified, notably the use of outdated terms and a lack of representation for some communities. ChatGPT demonstrates significant potential in providing accurate information on HIV-related topics. However, while responses were often scientifically accurate, they sometimes lacked the socio-political context and inclusivity essential for effective health communication. This underlines the importance of aligning AI-driven platforms with contemporary health communication strategies and ensuring the balance of accuracy and inclusivity.

BackgroundMalaysia, an upper middle-income country in the Asia-Pacific region, has an HIV epidemic that has transitioned from needle sharing to sexual transmission, mainly in men who have sex with men (MSM). MSM are the most vulnerable population for HIV in Malaysia. In 2022, our team developed a web-based artificial intelligence (AI) chatbot and tested its feasibility and acceptability among MSM in Malaysia to promote HIV testing. To enhance the usability of the AI chatbot, we made it accessible to the public through the website called MYHIV365 and tested it in an observational study.ObjectiveThis study aimed to test the usability of an AI chatbot in promoting HIV testing among MSM living in Malaysia.MethodsThis observational study was conducted from August 2023 to March 2024 among 334 MSM. Participants were recruited through community outreach and social-networking apps using flyers. The interactions between participants and the AI chatbot were documented and retrieved from the chatbot developer’s platform. Data were analyzed following a predefined metrics using R software (Posit Software, PBC).ResultsThe AI chatbot interacted with 334 participants, assisting them in receiving free HIV self-testing kits, offering information on HIV, pre-exposure prophylaxis (PrEP), and mental health, and providing details of 220 MSM-friendly clinics, including their addresses, phone numbers, and operating hours. After the study, 393 human-chatbot interactions were documented on the chatbot developer’s platform. Most participants (304/334, 91.0%) interacted with the AI chatbot once, 30 (9.0%) engaged 2 or more times at different intervals. Participants’ interaction time with the chatbot varied, ranging from 1 to 31 minutes. The AI chatbot properly addressed most participants’ questions (362/393, 92.1%) about HIV and PrEP. However, in 31 interactions, participants posed additional questions to the chatbot that were not programmed into the chatbot algorithms, resulting in unanswered interactions.ConclusionsThe web-based AI chatbot demonstrated high usability in delivering HIV self-testing kits and providing clinical information on HIV testing, PrEP, and mental health services. To enhance its usability in community and clinical settings, the chatbot must offer personalized health information and precise interaction, powered by sophisticated machine learning algorithms. In addition, establishing an effective connection between the AI chatbot and health care systems to eliminate stigma and discrimination toward MSM is crucial for the future implementation of AI chatbots.

HIV pre-exposure prophylaxis (PrEP) is a proven strategy for preventing HIV transmission, yet Black cisgender women remain underrepresented despite their elevated risk. Barriers such as stigma, mistrust, logistical challenges, and limited awareness hinder PrEP uptake in this population. Artificial intelligence (AI)-powered chatbot offers promising solutions by providing personalized, non-judgmental, and accessible education. This study aimed to identify barriers and facilitators to PrEP care among Black cisgender women and to explore stakeholder preferences for chatbot features. We conducted a qualitative study using semi-structured interviews with nine PrEP-eligible Black cisgender women and eight nurses providing HIV prevention services. Data were analyzed using grounded theory to identify themes related to PrEP barriers, chatbot features, and stakeholder preferences. Participants identified stigma, mistrust, financial challenges, and educational gaps as primary barriers to PrEP uptake. Chatbots were viewed as valuable tools for addressing these barriers by fostering privacy, providing tailored education, and enhancing accessibility. Black women emphasized the importance of empathy, conversational tones, and cultural sensitivity in chatbot design, while nurses highlighted the need for accuracy and robust privacy measures. Nurses were also seen as critical facilitators, bridging gaps in chatbot functionality and ensuring trust and cultural alignment. This study provides foundational insights for designing culturally sensitive, patient-centered chatbots.

Background: HIV/AIDS remains a major global public health challenge despite significant progress in treatment. New infections and HIV-related deaths persist, fueled by disparities in prevention and care access. Purpose: This review synthesizes recent advances across key domains - from vaccine development to novel treatments to omics approaches – that collectively hold promise for ending the HIV/AIDS pandemic. Main body: Multiple innovative HIV vaccine platforms are now in early-phase trials, including mRNA vaccines as well as conserved epitope and mosaic constructs for broader immunogenicity. Long-acting injectable antiretrovirals represent a major milestone in HIV treatment, while gene editing techniques offer future curative potential. Leveraging multi-omics data through genomics, transcriptomics, proteomics, and metabolomics provides systems-level insights into viral persistence and new therapeutic opportunities. The gut microbiome is increasingly recognized as a mediator of HIV progression, spurring research into probiotic/prebiotic supplementation and fecal transplantation. Across these domains, integration of artificial intelligence and machine learning will likely accelerate discovery. Conclusion: Despite past setbacks, the HIV cure effort has renewed momentum. Translating emerging tools like long-acting antiretrovirals and omics profiling into broader clinical application could bend the pandemic’s trajectory. Innovation must be paired with ensuring equitable access to maximize global impact.

As one of the core qualities of modern medical personnel training, the teaching paradigm of first aid competence is undergoing systematic changes with the deep integration of digital technology. Traditional first aid teaching faces problems such as insufficient reduction of clinical situation, imbalance of input-output ratio of teaching resources, and homogenization of evaluation dimension. The empowerment of digital technology provides an innovative path to solve these dilemmas - building a high-fidelity training environment through virtual simulation, real-time interaction and feedback with the help of intelligent devices, and accurate teaching and evaluation based on data analysis, so as to promote the transformation of first-aid education to the direction of intelligence, personalization, and precision. In this study, a set of intelligent first aid education system is constructed with deep learning (DL), virtual reality (VR) and augmented reality (AR) technologies as the core. The system adopts deep reinforcement learning (DRL) algorithm to optimize the dynamic response logic of the virtual simulation scene, collects physiological data in real time through multimodal sensor network for injury feature extraction, and combines AR technology to construct a three-dimensional interactive first aid environment. Through virtual simulation technology scenario simulation, intelligent simulator real-time feedback, artificial intelligence assisted assessment, combined with case studies and teamwork exercises and other teaching methods, to build an immersive, personalized first aid skills training mode, so as to comprehensively improve the clinical first aid resilience and teamwork level of medical students, and to promote the development of medical education in the direction of intelligence and precision.