ArticleScientific reports2025
Healthcare professionals and the public sentiment analysis of ChatGPT in clinical practice.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Use of chat GPT for sentiment and readability analysis of nutrition articles in legacy media.JAMIA open · 2026Article
- Comparison of Emotional Content in Text Responses From Physicians and AI Chatbots to Patient Health Queries: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Mapping Clinical Intent in Internet Hospitals: Identifying and Profiling Patient Demand via Natural Language Processing.Computational and structural biotechnology journal · 2026Article
- An academic evaluation of ChatGpt's ability and accuracy in creating patient education resources for rare cardiovascular diseases.Scientific reports · 2025Article
- Global Health care Professionals' Perceptions of Large Language Model Use In Practice: Cross-Sectional Survey Study.JMIR medical education · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To explore the attitudes of healthcare professionals and the public on applying ChatGPT in clinical practice. The successful application of ChatGPT in clinical practice depends on technical performance and critically on the attitudes and perceptions of non-healthcare and healthcare. This study has a qualitative design based on artificial intelligence. This study was divided into five steps: data collection, data cleaning, validation of relevance, sentiment analysis, and content analysis using the K-means algorithm. This study comprised 3130 comments amounting to 1,593,650 words. The dictionary method showed positive and negative emotions such as anger, disgust, fear, sadness, surprise, good, and happy emotions. Healthcare professionals prioritized ChatGPT's efficiency but raised ethical and accountability concerns, while the public valued its accessibility and emotional support but expressed worries about privacy and misinformation. Bridging these perspectives by improving reliability, safeguarding privacy, and clearly defining ChatGPT's role is essential for its practical and ethical integration into clinical practice.
Indexed as
Identifiers
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.