An Explainable Multimodal Deep Learning Framework for Early Disease Prediction Using Clinical and Medical Imaging Data

Abstract

Early disease prediction benefits from combining complementary evidence contained in clinical records and medical images. This paper develops an explainable multimodal deep learning framework integrating structured clinical variables, image representations, calibrated risk estimation and clinically interpretable explanations. The proposed architecture combines a missingness-aware tabular encoder with a pretrained image backbone, cross-modal gated fusion and a supervised prediction head. Patient-level partitioning, site-aware validation, subgroup evaluation and explanation-fidelity tests are specified to reduce leakage and misleading interpretation. We define preprocessing, learning objectives, uncertainty handling and deployment governance, and present a fully reproducible evaluation protocol. Because no patient-level dataset or trained model outputs were supplied, the numerical tables and figures in this manuscript are explicitly illustrative synthetic examples, not experimental evidence or clinical performance claims. The framework offers a research-ready foundation for future retrospective and prospective validation, emphasizing transparent reporting, calibration and safe human oversight.