ArticleAIDS and behavior2025
HIV Prevention and Treatment Information from Four Artificial Intelligence Platforms: A Thematic Analysis.
Article in AIDS and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Generative Artificial Intelligence for Qualitative Methods in Health Research: Rapid Review.Journal of medical Internet research · 2026Review
- Artificial intelligence in HIV research: a structured review and task-oriented clinical framework.Frontiers in digital health · 2026Review
- A turning point for youth HIV care in the United States: tailored strategies for adolescents and young adults.Critical public health · 2026Article
- AI-Driven Large Language Models in Health Consultations for HIV Patients.Journal of multidisciplinary healthcare · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Health information is highly accessible with the prominence of artificial intelligence (AI) platforms, such as Chat Generative Pre-Trained Transformer (ChatGPT). Within the context of human immunodeficiency virus (HIV), it is paramount to understand and evaluate the information being provided by AI platforms concerning the safety, side effects, and efficacy of medications to prevent and treat HIV. Prompts (n = 38) requesting information regarding HIV medication use for prevention and treatment were inputted into three AI-based Large Language Models (LLMs; ChatGPT 3.5, ChatGPT 4.0, Google Bard [now Gemini]) and one chatbot (HIV.gov Chatbot) on four consecutive weeks. Outputs (n = 608) were recorded verbatim, weekly by platform. Qualitative analyses using a conventional content analysis coding approach examined key themes in responses; response comprehensiveness was rated via the number of themes represented in a response. Core themes emerged across prompts. A recommendation to speak with a medical professional for further information was the most common theme across platforms. Organ/bone side effects were the most prevalent side effect. Responses pointed to medication efficacy to prevent and treat HIV. ChatGPT 4.0 provided the most comprehensive responses across platforms, while the HIV.gov Chatbot gave the least comprehensive information. Health information on HIV medication safety, side effects, and efficacy is widely available using AI platforms. Results indicate that AI responses typically included recommendations to consult a medical professional to personalize care. The efficacy of medications was never questioned across AI platforms. Future research directions for AI use within the context of HIV prevention and care are provided.
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Registered trials
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.