Multimodal Models in Healthcare: Methods, Challenges, and Future Directions for Enhanced Clinical Decision Support

Abstract

Decision-making in modern healthcare increasingly relies on integrating a variety of data sources, including patient demographics, medical imaging, laboratory results, clinical narratives, and temporal data, all of which are difficult for traditional computational methodologies to accurately predict. This paper evaluates the latest methodologies that integrate diverse data types, including photographs, clinical notes, temporal measurements, and structured tables, through techniques such as feature amalgamation, prioritization of essential information, and utilization of graphs. We also assess pre-training, fine-tuning, and comprehensive evaluation of model generation procedures. By synthesizing findings from 50 of 91 peer-reviewed papers published between 2020 and 2024, we demonstrate that the integration of structured and unstructured data significantly improves performance in tasks like diagnosis, prognosis prediction, and personalized treatment. This review combines substantial multimodal datasets and applications across several therapeutic domains while addressing critical issues such as data heterogeneity, scalability, interpretability, and ethical considerations. This paper highlights the transformative potential of multimodal models in improving clinical decision support, providing a framework for future research to advance precision medicine and enhance healthcare outcomes.

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