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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.

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

The model can effectively identify good quality skin images but needs more testing for real-world use.

This study reports that improvements to the EczemaNet pipeline, incorporating pixel-level segmentation and data augmentation, enhanced the reliability and interpretability of assessing atopic dermatitis severity from digital images.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.