ArticleNPJ digital medicine2025
Synthetic medical education in dermatology leveraging generative artificial intelligence.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Attitudes Toward Large Language Models in Health Care and Preferences for Their Adoption and Oversight Among Health Care Professionals: Cross-Sectional Survey.Journal of medical Internet research · 2026Article
- A Multimodal AI Framework for Medical Education: Integrating Adaptive Image Retrieval, Fast Synthesis, and LLM-Based Clinical Auditing.Journal of imaging · 2026Article
- Evaluating Source-Based Large Language Models for Preclinical Dermatology Education: Comparative Study.JMIR formative research · 2026Article
- Large Language Model Performance and Clinical Reasoning Tasks.JAMA network open · 2026Article
- Preparing for Pandemics with Large Language Models: An Evaluation of Sensitivity Across COVID-19, Zika, and Monkeypox Case Reports.Journal of medical systems · 2026Article
- Generative Artificial Intelligence for Medical Image Creation in Health Professions Education: a Scoping Review.Journal of medical systems · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- The Digital Standardized Patient: An Artificial Intelligence Coach for Cultural Dexterity in Surgical Care.Journal of the American College of Surgeons · 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
10 authors.
Funding
Abstract
The advent of large language models (LLMs) represents an enormous opportunity to revolutionize medical education. Via "synthetic education," LLMs can be harnessed to generate novel content for medical education purposes, offering potentially unlimited resources for physicians in training. Utilizing OpenAI's GPT-4, we generated clinical vignettes and accompanying explanations for 20 skin and soft tissue diseases tested on the United States Medical Licensing Examination. Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r = 0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. This study underscores the potential of LLMs in enhancing the scalability, accessibility, and customizability of dermatology education materials. Efforts to increase vignettes' demographic diversity should be incorporated to increase applicability to diverse populations.
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.