ArticleCureus2025
Evaluation of the Accuracy of Artificial Intelligence (AI) Models in Dermatological Diagnosis and Comparison With Dermatology Specialists.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.JID innovations : skin science from molecules to population health · 2026Review
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- When AI and Experts Agree on Error: Intrinsic Ambiguity in Dermatoscopic Images.Journal of imaging · 2026Article
- Impact of Large Language Model-assisted Differential Diagnosis on Clinical Decision-making in Dermatology: A Feasibility Study Using ChatGPT-5.Acta dermato-venereologica · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in generative artificial intelligence (AI) have expanded its applications in diagnostic support within dermatology, but its clinical accuracy requires ongoing evaluation. This study compared the diagnostic performance of three advanced AI models, ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, with that of board-certified dermatologists, using a dataset of 30 cases encompassing a variety of dermatological conditions. The AI models demonstrated diagnostic accuracy comparable to, and sometimes exceeding, that of the specialists, particularly in rare and complex cases. Statistical analysis revealed no significant difference in accuracy rates between the AI models and dermatologists, indicating that AI may serve as a valuable supplementary diagnostic tool in dermatological practice. Limitations include a small sample size and potential selection bias. However, these findings underscore the progress in AI's diagnostic capabilities, supporting further validation with larger datasets and diverse clinical scenarios to confirm its practical utility.
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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.