Explainable Multimodal AI Framework For Early Disease Prediction Using Clinical And Imaging Data

Abstract

Early disease prediction is crucial in enhancing patient care by providing early diagnoses, personalized therapy and effective clinical decision making. Even though recent advancements in artificial intelligence have greatly improved disease predictability, the current methods facilitate mostly by using either structured clinical data or medical imaging data alone, which restricts the precision of diagnosis and decreases the understanding of the model. Moreover, the absence of open decision-making processes limits the implementation of AI systems in the actual healthcare setting. To solve these issues, the paper will present an Explainable Multimodal AI Framework to Early Disease Prediction with Clinical and Imaging Data. The offered framework combines structured clinical data with medical imaging by way of a multimodal feature extraction pipeline, and an explainable feature fusion plan that harmonizes the cross-modal diagnostic information provided by both modalities. It also includes an explainability module to offer clear predictions by determining which clinical features are influential and which areas in the image are diagnostically relevant to assist clinicians in interpreting them and build trust in AI-assisted diagnosis. The framework is experimentally tested on publicly available clinical and medical imaging data, and correlated with representative machine learning and deep learning baseline models. The standard classification metrics, explainability measures, and computational efficiency indicators are used as the measurements of performance. Through experimental findings, it is established that the proposed framework continuously delivers high predictive performance, with stable and clinical meaningful explanations. The proposed framework offers an efficient and precise, explainable, and practical way to predict the onset of an early disease, which is why combining multimodal learning and explainable artificial intelligence will help build trustworthy artificial intelligence-based clinical decision support systems and enhance intelligent healthcare applications.