Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions

TL;DR

This paper reviews recent progress in Medical Multimodal Foundation Models (MMFMs), highlighting their applications in clinical diagnosis and treatment, the challenges faced, and future research directions to enhance healthcare outcomes.

Contribution

It provides a comprehensive analysis of recent developments in MMFMs, focusing on datasets, architectures, and clinical applications, and discusses future opportunities in healthcare.

Findings

MMFMs improve diagnostic accuracy and treatment personalization.

Large-scale multimodal datasets accelerate model development.

Challenges include optimizing multimodal representations and clinical integration.

Abstract

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.