The study, published in BMC Health Services Research , examined both reported user experiences and community responses. Researchers used BERTopic, a topic-modeling method, to identify recurring themes in the retained posts, producing 14 themes. They then coded posts along four dimensions: the purpose of AI use, emotional change attributed to chatbot responses, trust stance and verification orientation, and the reported relationship to professional care.

A separate classification of 43,899 comments assigned them to nine response functions. These automated classification tasks were validated against human-coded samples, with reported accuracy ranging from 0.79 to 0.94 and macro-F1 scores from 0.68 to 0.91. Emotion was divided in the accounts posted by users.

In 26.3% of retained posts, authors said chatbot responses changed how they felt: 12.6% described relief and 11.5% described alarm. These percentages represent emotional changes attributed by post authors, not measured effects caused by the chatbots. The analysis also found substantial caution and information checking.

In 42.4% of posts, authors expressed distrust or caution about chatbot advice or said they continued checking it against other sources, whereas 8.9% described treating a chatbot as their main source. The findings therefore portray users comparing AI responses with other health information rather than consistently accepting them as definitive. Comments revealed differences among the selected communities.

Among comments assigned a substantive response function, sharing health information or personal experience was most common in the medical-advice subreddits and in r/ClaudeAI. In three other AI-product subreddits, the most frequent responses presented chatbots as limited aids or warned about their limitations. In r/HealthAnxiety, the most common response function discouraged repeated reassurance seeking.

The study therefore documents how communities interpreted and contextualized AI-generated health advice, but it does not show that any particular community response improved health outcomes. Overall, the research offers a bounded snapshot of self-reported behavior in eight selected Reddit communities rather than a measure of the general public or a clinical evaluation of chatbot performance. Because the corpus consisted of public Reddit posts and comments, the results cannot establish whether chatbot advice caused the reported feelings, whether it was medically accurate or whether it affected diagnoses or treatment decisions.

The analysis also found that professional-care pathways dominated the selected accounts: 24.0% of posts described seeking, continuing or obtaining more urgent care, compared with 4.6% that described replacing, delaying, avoiding or reducing care. Within this corpus, generative AI was most frequently presented not as an autonomous doctor substitute, but as an interpretive tool whose output users questioned, compared with other information and discussed with peers.

Subject of Research: Content analysis of Reddit experiences with and community responses to generative AI health advice

Article Title: User experiences and community responses to generative artificial intelligence health advice on reddit: topic modeling and content analysis study

Article References: Tang, Z., He, G., & Pan, X. (2026). User experiences and community responses to generative artificial intelligence health advice on reddit: topic modeling and content analysis study. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15613-9

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15613-9

Keywords: generative artificial intelligence, health chatbots, Reddit, online health information, health anxiety, trust in AI, professional care, topic modeling, BERTopic, care-seeking, User, experiences