
Letter
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This review explores current applications of AI in CD, drawing from 12 original studies that investigated AI-based image analysis, biomarker discovery, and patient risk profiling. Convolutional neural networks demonstrated high diagnostic accuracy (up to 99.5%) in interpreting patch test images, while ML algorithms successfully identified transcriptomic signatures distinguishing allergic CD from irritant CD. In addition, AI has been used to predict positive patch test outcomes and identify high-risk patients based on clinical and occupational factors.
Despite these promising developments, limitations such as dataset bias, lack of standardization, and model interpretability remain. Nevertheless, AI represents a transformative tool in dermatology, offering the potential for standardized diagnostics, personalized care, and enhanced accessibility.



