多模态大模型在重大疾病领域的研究综述

A review of multimodal large models in the field of major diseases

摘要:

癌症、心脑血管疾病、神经退行性疾病等重大疾病的防控是现代医学的核心挑战,其精准诊疗高度依赖对医学影像、电子病历、基因组学等多源异构信息的综合研判。然而,传统单模态分析方法存在信息孤岛,难以全面刻画疾病的复杂生物学机制与临床表型。为应对此挑战,本文系统综述了多模态大模型在重大疾病防控中的研究进展。文中对多模态大模型在重大疾病领域的研究进展进行了全面综述。首先,概括了以Transformer为核心的技术范式,阐明其融合多模态医疗数据的底层架构与协同机制。其次,系统介绍了该模型在早期诊断、精准分型、预后预测等核心临床场景中的应用现状,并深入剖析其技术潜力与实证价值；进而,归纳总结了当前面临的数据异构性、模型“黑箱”问题、伦理法规与数据安全等共性挑战。最后,展望了未来发展趋势,重点提出了面向临床任务的专用模型优化、因果推理与可解释性增强、联邦学习与隐私计算以及人机协同智能诊疗等关键突破口。本综述旨在为科研人员、临床医生与政策制定者提供系统性参考,推动多模态大模型在重大疾病防治中的转化落地,赋能精准医疗高质量发展。

Abstract:

The prevention and control of major diseases, such as cancer, cardiovascular and cerebrovascular diseases, and neurodegenerative disorders, remain core challenges in modern medicine. Their precise diagnosis and treatment critically rely on the integrative analysis of heterogeneous multi-source data, including medical imaging, electronic health records, and genomics. Traditional unimodal approaches, however, suffer from information silos and struggle to comprehensively characterize the complex biological mechanisms and clinical phenotypes of diseases. In response to this challenge, this paper systematically reviews the progress of multimodal large models (MLM) in major disease prevention and control. First, we summarize the transformer-centered technical paradigm, elucidating the underlying architecture and synergistic mechanisms that enable fusion of multimodal medical data. Second, we systematically survey applications across core clinical scenarios-early diagnosis, precise subtyping, and prognostic prediction, while deeply analyzing its technical potential and empirical value. Furthermore, we summarize common challenges encountered in practice, including data heterogeneity, the model “black box” problem, and ethical, legal, and data security issues. Finally, we outlook future development trends and propose key breakthrough directions, emphasizing clinically task-oriented model optimization, causal reasoning and enhanced interpretability, federated learning and privacy-preserving computation, and human-AI collaborative intelligent diagnostics. This review aims to provide a systematic reference for researchers, clinicians, and policymakers, promoting the clinical translation of multimodal large models in the prevention and treatment of major diseases, thereby empowering the high-quality development of precision medicine.