ArticleDermatology reports2025
Application of ChatGPT as a content generation tool in continuing medical education: acne as a test topic.
Article in Dermatology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Preparedness for generative AI adoption among Chinese cancer survivors: a multi-center cross-sectional survey study.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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The large language model (LLM) ChatGPT can answer open-ended and complex questions, but its accuracy in providing reliable medical information requires a careful assessment. As part of the AI-CHECK (Artificial Intelligence for CME Health E-learning Contents and Knowledge) study, aimed at evaluating the potential of ChatGPT in continuous medical education (CME), we compared ChatGPT-generated educational content to the recommendations of the National Institute for Health and Care Excellence (NICE) guidelines on acne vulgaris. ChatGPT version 4 was exposed to a 23-item questionnaire developed by an experienced dermatologist. A panel of five dermatologists rated the answers positively in terms of "quality" (87.8%), "readability" (94.8%), "accuracy" (75.7%), "thoroughness" (85.2%), and "consistency" with guidelines (76.8%). The references provided by ChatGPT obtained positive ratings for "pertinence" (94.6%), "relevance" (91.2%), and "update" (62.3%). The internal reproducibility was adequate both for answers (93.5%) and references (67.4%). Answers related to issues of uncertainty and/or controversy in the scientific community scored the lowest. This study underscores the need to develop rigorous evaluation criteria for AI-generated medical content and for expert oversight to ensure accuracy and guideline adherence.
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.