Public perceptions of AI in healthcare
A large-scale Bertopic and sentiment analysis of Reddit discussions
Bibliographic Data
| ID | 22077151 |
|---|---|
| Authors | Zaiyu Tang (Xi'an Jiaotong University), Wenbao Ma (Xi'an Jiaotong University), Zhipeng Bai (0000-0002-1816-2353, Shanxi Academy of Building Research), Jiahe Liang (0009-0001-1319-3433, Dalian Medical University, corresponding author), Yandong Xie (United States Air Force, corresponding author) |
| Year | 2026 |
| Volume | 14 |
| Pages | 1839898-1839898 |
| Publication date | 2026-06-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2026.1839898 |
| PMID | 42312001 |
| OpenAlex | W7163134604 |
| Language | EN |
| References cited | 72 |
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
Content analysis · Perception · Public health · Public opinion · Sentiment analysis · Social media · Thematic analysis · Topic model · Workforce · Artificial Intelligence in Healthcare and Education · Electronic Health Records Systems · Ethics and Social Impacts of AI
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| Citation velocity | historical |
|---|---|
| Highly cited | No |