医学视觉语言模型融合影像与文本，提升诊疗效率与智能水平。
Vision Language Models in Medicine
- 将通用视觉语言模型适配医学任务，融合影像与病历文本数据。
- 在临床决策、医学教育中展现潜力，但面临数据稀缺与可解释性挑战。
- 适合医疗AI研究者与临床工程师参考，关注模型伦理与落地瓶颈。
随着视觉语言模型（VLMs）的发展，医学人工智能迎来技术突破与范式变革。本文系统综述了医学视觉语言模型（Med-VLMs）的最新进展，该模型通过整合视觉与文本数据以提升医疗成效。文章剖析了其基础技术，说明通用模型如何被适配用于复杂医学任务，并探讨其在临床实践、医学教育与患者照护中的应用。尽管Med-VLMs对医疗流程具有变革性影响，但仍存在数据稀缺、任务泛化能力弱、可解释性差及公平性、问责制与隐私等伦理问题。这些问题因数据分布不均、计算需求高与监管障碍而加剧。建立严格的评估方法与稳健的监管框架对安全集成至医疗工作流至关重要。未来方向包括利用大规模多样化数据集、提升跨模态泛化能力、增强可解释性。联邦学习、轻量级架构与电子健康记录（EHR）整合被视为推动普惠化与临床相关性的关键路径。本综述旨在全面揭示Med-VLMs的优势与局限，促进其在医疗领域的伦理化与均衡化应用。
原文摘要 · Abstract (English)
With the advent of Vision-Language Models (VLMs), medical artificial intelligence (AI) has experienced significant technological progress and paradigm shifts. This survey provides an extensive review of recent advancements in Medical Vision-Language Models (Med-VLMs), which integrate visual and textual data to enhance healthcare outcomes. We discuss the foundational technology behind Med-VLMs, illustrating how general models are adapted for complex medical tasks, and examine their applications in healthcare. The transformative impact of Med-VLMs on clinical practice, education, and patient care is highlighted, alongside challenges such as data scarcity, narrow task generalization, interpretability issues, and ethical concerns like fairness, accountability, and privacy. These limitations are exacerbated by uneven dataset distribution, computational demands, and regulatory hurdles. Rigorous evaluation methods and robust regulatory frameworks are essential for safe integration into healthcare workflows. Future directions include leveraging large-scale, diverse datasets, improving cross-modal generalization, and enhancing interpretability. Innovations like federated learning, lightweight architectures, and Electronic Health Record (EHR) integration are explored as pathways to democratize access and improve clinical relevance. This review aims to provide a comprehensive understanding of Med-VLMs' strengths and limitations, fostering their ethical and balanced adoption in healthcare.
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