A Comprehensive Review of Alopecia Areata Detection Using Self-Supervised Auto ML Models: Systematic Insights and Emerging Challenges

This study reviews the use of self-supervised Auto ML models for detecting alopecia areata, an immune-mediated condition causing non-scarring hair loss. It highlights the effectiveness of these models in automated diagnosis, while also addressing challenges such as model explainability and data bias. The review analyzes various datasets and performance metrics, noting significant advancements in AI-driven dermatological diagnostics and suggesting future directions for improving alopecia areata detection.