Multimodal Vision-Language Models for Explainable Clinical Document Understanding and Medical Image-Based Decision Support

Abstract

Modern clinical systems produce heterogeneous records that combine narrative notes, structured tables, scanned forms, medical images, and operational data. The integration of vision and language in artificial intelligence has opened new possibilities for clinical document understanding and image-based decision support, but it also raises substantial questions about explainability, governance, fairness, and deployment. This paper examines multimodal vision-language models from an interdisciplinary systems perspective, focusing on how architectural choices interact with clinical accountability. It analyzes the alignment of visual and textual representations, the role of layout-aware document understanding, the grounding of medical image interpretation in contextual evidence, and the structural trade-offs between model complexity and transparency. The discussion emphasizes that explainability should not be treated as a post hoc interface but must be embedded in architecture, evaluation, institutional workflows, and regulatory practice. The paper further considers data governance, bias, sustainability, and policy implications, arguing that clinical adoption depends on robust evidence tracing, adversarial auditing, and adaptive oversight rather than predictive performance alone. The analysis connects recent technical advances to the operational realities of health systems and concludes with forward-looking governance recommendations for accountable multimodal clinical systems.