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This study presents a new automated computer vision system to objectively measure periocular hair density changes in breast cancer patients undergoing chemotherapy, demonstrating high precision in tracking individual changes and potential as a reliable tool for future clinical trials.

This study assessed an AI-based mobile app for evaluating androgenetic alopecia severity, finding that it achieved 94% accuracy compared to human dermatologists, while participants often underestimated their own hair loss severity.

This study used bibliometric analysis to evaluate global research on AI applications in dermatology, identifying 406 relevant papers and highlighting current priorities such as machine learning for wound progression, AI in teledermatology, and applications for skin diseases.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.