AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection

TL;DR

This paper presents a comprehensive AI framework that combines image analysis and family history data to improve dermatological diagnosis, aiming for clinical integration and enhanced accuracy for hereditary skin conditions.

Contribution

It introduces an interpretable multi-modal AI system integrating deep learning and clinical data, validated through prospective trials for better skin disease detection.

Findings

Enhanced diagnostic accuracy with family history data

Potential for early detection of hereditary skin conditions

Framework designed for clinical workflow integration

Abstract

Dermatological conditions affect 1.9 billion people globally, yet accurate diagnosis remains challenging due to limited specialist availability and complex clinical presentations. Family history significantly influences skin disease susceptibility and treatment responses, but is often underutilized in diagnostic processes. This research addresses the critical question: How can AI-powered systems integrate family history data with clinical imaging to enhance dermatological diagnosis while supporting clinical trial validation and real-world implementation? We developed a comprehensive multi-modal AI framework that combines deep learning-based image analysis with structured clinical data, including detailed family history patterns. Our approach employs interpretable convolutional neural networks integrated with clinical decision trees that incorporate hereditary risk factors. The methodology includes prospective clinical trials across diverse healthcare settings to validate AI-assisted diagnosis against traditional clinical assessment. In this work, validation was conducted with healthcare professionals to assess AI-assisted outputs against clinical expectations; prospective clinical trials across diverse healthcare settings are proposed as future work. The integrated AI system demonstrates enhanced diagnostic accuracy when family history data is incorporated, particularly for hereditary skin conditions such as melanoma, psoriasis, and atopic dermatitis. Expert feedback indicates potential for improved early detection and more personalized recommendations; formal clinical trials are planned. The framework is designed for integration into clinical workflows while maintaining interpretability through explainable AI mechanisms.