A Multimodal, Explainable, and Bias-Aware AI System for Inclusive Skin Disease Risk Prediction

Skin manifestaions are common and frequently necessitate at least accurate diagnosis if specific treatment is not obtained. Conventional methods of diagnosis may be subjective, time-consuming and inconsistent. In this paper, we present an AI-based system for skin disease classification that integrates dermoscopic image analysis, patient demographics, and medical history running on a Flask web server. The model is a deep convolutional neural network (CNN), which has been trained on repositories of skin diseases to predict the states based on the images given.The Grad- CAM heatmaps are provided to clinicians which indicate regions of interest for providing interoperability, while brief and simple textual summaries are given to patients as easily understandable explanations. The system is made which gathers patient information like name, age, gender, medical history and the predictions are made to formulate a comprehensive, multimodal diagnostic report. The Results shows that the solution enhances not only diagnostic accuracy but also the trust, interpretability and inclusiveness in healthcare area.