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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.

A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.

Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.

This study suggests that stress intensity is highly correlated with hairfall severity, highlighting the potential of an inexpensive and accessible machine learning approach for forecasting and prevention.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.