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This study used artificial neural networks to predict hair loss by analyzing factors like gender and zinc deficiency, suggesting neural networks may effectively model hair loss prediction.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.