Explainable artificial intelligence for risk assessment and personalized management of atopic dermatitis

This paper presents a new risk prediction model for Atopic Dermatitis (AD) using Explainable Artificial Intelligence (XAI) to aid in personalized management planning. With a multimodal dataset consisting of over 50,000 dermatology records, which includes clinical notes, patient history, and skin images, the model obtains a remarkable predictive accuracy of 91.2% with an F1-score of 0.89. To trust the model and its outputs, the explainable AI frameworks SHAP and LIME were incorporated, allowing clinicians to understand how the model made its predictions and to confirm the reasoning behind its decisions. The system outlines the most important risk factors that must be addressed, such as genetic factors, environmental influences, and the patient's adherence to approved therapies, thus enabling personalized responsive care. Pilot studies with dermatologists showed that support for treatment recommendations improved by 35% and that misdiagnosis dropped by 27% after the model's recommendations were integrated, which highlights the model's potential clinical utility. The model's design focuses on easy scalability and interpretability which may transform the delivery of care for AD through telemedicine in underserved and resource-limited healthcare systems that need accurate, explainable, flexible, and responsive decision support systems.