ArticleFrontiers in public health2026
Public perceptions of AI in healthcare: a large-scale BERTopic and sentiment analysis of Reddit discussions.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Acceptance of generative AI-assisted medical decision-making among Chinese physicians and patients and its ethical determinants: a cross-sectional survey.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Public perception plays an important role in the responsible implementation of artificial intelligence (AI) in healthcare because trust, perceived risk, and expectations regarding human-AI collaboration may influence the acceptance of AI-assisted medical services. This study aimed to examine public discourse and sentiment regarding AI in healthcare using large-scale Reddit discussions. Methods: We conducted a retrospective content analysis of 36,555 Reddit posts and comments published between March 1, 2020, and March 31, 2025. Reddit was used as a source of large-scale, spontaneous, user-generated discussions. BERTopic modeling was applied to identify latent discussion topics. Topics were interpreted based on semantic similarity, representative keywords, and representative paraphrased posts, and were subsequently grouped into thematic domains. Sentiment analysis and temporal trend analysis were also performed. Results: Fourteen discussion topics were identified across six thematic domains: human-centered healthcare, auxiliary medical services, AI platforms and tools, cultural perceptions, food and health safety, and medical regulation. Overall sentiment distribution was 41.4% positive, 23.8% neutral, and 35.1% negative, indicating a generally positive orientation while also revealing substantial public concern. Negative sentiments were primarily associated with technological maturity, commercialization, privacy and safety risks, and the potential displacement of physicians. Temporal analysis demonstrated changes in sentiment distribution over time, particularly following the widespread public diffusion of generative AI tools. Discussion: The findings suggest that public attitudes toward medical AI are simultaneously optimistic and cautious. Concerns regarding governance, safety, commercialization, and workforce implications remain prominent in online discussions. These results highlight the importance of transparent communication, clearer regulatory governance, and careful workforce planning to support the responsible integration of AI into healthcare systems.
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What OpenQuestion holds
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.