September 2022 in “Research Square (Research Square)” In this study, the DIET-AI model, developed from a large dataset of over 200,000 images, demonstrated diagnostic performance for 31 skin diseases comparable to dermatologists of varying experience levels in 15 hospitals across China, supporting its potential effectiveness in clinical settings.
51 citations
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April 2021 in “JAMA network open” This study found that artificial intelligence assistance improved diagnostic accuracy in dermatologic cases for primary care physicians and nurse practitioners, with higher agreement rates with reference diagnoses observed.
September 2025 in “The Open Dermatology Journal” In this study, the Tibot AI application showed high diagnostic accuracy for adnexal and pigmentary disorders and cutaneous tumors in dermatology, but was less effective for immunological disorders and infestations, suggesting the need for further dataset refinement.
4 citations
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January 2021 in “Dermatologic Therapy” This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.
This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.
