医学多模态大模型助力精准诊疗，提升诊断与治疗效率
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
- 构建多器官多模态数据驱动的通用医疗大模型
- 支持早期诊断到个性化治疗的全流程临床应用
- 适合医疗AI研究者与临床决策系统开发者参考
深度学习的进步显著推动了临床诊断与治疗的革新，为提升各临床领域的诊断精度和治疗效果提供了新路径，助力精准医疗发展。多器官、多模态数据集的日益丰富加速了大规模医学多模态基础模型（MMFMs）的发展。这些模型具备强大的泛化能力与丰富的表征能力，正被广泛应用于从早期诊断到个性化治疗策略的各类临床任务。本文全面分析了近期MMFMs的进展，聚焦数据集、模型架构与临床应用三大方面，探讨多模态表征优化中的挑战与机遇，并展望其如何通过改善患者结局与优化临床流程重塑未来医疗。
原文摘要 · Abstract (English)
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.
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