AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection
Satya Narayana Panda, Vaishnavi Kukkala, Spandana Iyer
TL;DR
The paper proposes a comprehensive multimodal framework for dermatological diagnosis that fuses high-resolution clinical images with structured data, including family history, using an interpretable architecture designed for real-world clinical integration. It details a four-component system comprising a Multi-Modal AI Engine, an Interpretability Layer, a Clinical Integration Module, and a Continuous Learning System, along with a four-phase implementation plan and planned prospective clinical validation. The approach targets superior diagnostic accuracy, rapid analysis, and high interpretability to support clinician decision-making and reduce unnecessary referrals, with anticipated substantial improvements in diagnostic speed and patient outcomes. By emphasizing workflow compatibility, privacy, and scalable deployment, the work aims to bridge the gap between laboratory AI capabilities and practical dermatology care across diverse settings.
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.