Multi-Modal Clinical Document Understanding via Joint Text–Image Representations

Abstract

Multi-modal clinical document understanding has emerged as a critical area of investigation, aiming to improve patient outcomes, aid clinical decision-making, and streamline healthcare workflows by leveraging multiple sources of information. These sources include textual reports, physician notes, and diagnostic images such as X-ray, CT, and MRI scans. Traditional approaches for interpreting clinical data have predominantly focused on either text or images independently, missing valuable insights that can emerge from the synergy of textual and visual features. Recent advances in deep learning now enable the integration of diverse data streams, providing a more holistic view of patient conditions and reducing diagnostic uncertainty. However, effective multi-modal representation still poses several challenges, such as aligning high-dimensional data from heterogeneous domains, handling sparse and noisy clinical notes, and integrating large-scale datasets without overfitting. This work explores the theoretical foundations, methodological designs, and practical implementations of multi-modal systems for clinical document understanding, with a particular emphasis on joint text–image representations. By blending state-of-the-art natural language processing techniques with robust image feature extraction modules, we examine how models can capture latent relationships across modalities and how structured representations can be employed for domain-specific reasoning tasks. Our approach aspires to push the boundaries of current capabilities, ultimately enabling comprehensive and context-aware analyses of complex clinical datasets for improved patient care.