3 citations
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March 2024 in “arXiv (Cornell University)” This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.
January 2026 in “Diagnostics” This study reports that publicly available large language models are currently less accurate than human experts in diagnosing trichoscopic images, suggesting the need for further development and specialized training for these AI tools in trichology.
6 citations
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February 2024 in “JAAD International” ChatGPT is preferred for creating dermatology patient handouts, but all models can be useful with oversight.
March 2026 in “ArXiv.org” This review presents a comprehensive evaluation of medical reasoning using large language models, highlighting a significant gap between exam-level performance and true clinical decision-making accuracy.
January 2026 in “China CDC Weekly” This study explored using large language models to automatically identify monkeypox cases from electronic medical records, finding that models based on DeepSeek features performed better than traditional methods, with logistic regression showing high accuracy in detecting key symptoms like fever and rash.