医疗保健中的医学世界模型：可信临床翻译的基础、应用与挑战

中文摘要 AI 辅助

医学世界模型通过表示不断演变的患者状态并对其随时间变化以及对临床干预的反应进行建模，为扩展医疗人工智能超越静态预测提供了一个框架。本综述通过结构化叙述性综合和可重复证据来定义该领域的概念边界、技术基础、应用领域和证据要求。筛选了1455条独特记录，汇编了98个来源的语料库，包括14项符合医学世界模型严格实证定义的研究。该领域围绕患者状态表示、时间动态建模、干预条件模拟和临床医生监督规划这四种能力组织。证据涵盖医学成像、纵向电子健康记录等多个方面。研究为轨迹预测和候选干预比较提供了技术可行性的早期证据，但大多仍为回顾性、特定任务或临床前的。证据基础还受到多种因素限制，临床翻译将依赖于精确的干预表示等多方面。

英文摘要

Medical world models offer a framework for extending medical artificial intelligence beyond static prediction by representing evolving patient states and modelling how they change over time and in response to clinical interventions. This Review defines the conceptual boundaries, technical foundations, application domains, and evidence requirements of the field through a structured narrative synthesis with reproducible evidence mapping. We screened 1,455 unique records and assembled a corpus of 98 sources, including 14 studies that met a strict empirical definition of a medical world model. The field is organised around four capabilities: patient state representation, temporal dynamics modelling, intervention-conditioned simulation, and clinician-supervised planning. Evidence spans medical imaging, longitudinal electronic health records, treatment response modelling, physiological and multimodal state modelling, ultrasound and surgical interaction, and population and health-system simulation; clinical digital twins are treated as a cross-cutting integration framework. Current studies provide early evidence of technical feasibility for trajectory forecasting and comparison of candidate interventions, but most remain retrospective, task-specific, or preclinical. The evidence base is further limited by incomplete longitudinal intervention data, inconsistent action semantics, limited causal identifiability, long-horizon error accumulation, inadequate uncertainty estimation, and limited external validation. Clinical translation will therefore depend on precise intervention representations, robust causal and mechanistic grounding, calibrated trajectory-level uncertainty, safety-constrained planning, and prospective multicentre validation against clinically meaningful endpoints.

发表机构

• National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University（大连大学软件工程学院计算机辅助设计国家地方联合工程实验室）
• Department of Radiology, Xinhua Hospital Affiliated to Dalian University（大连大学附属新华医院放射科）
• The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
• University of California, San Francisco（加州大学旧金山分校）
• Yale University（耶鲁大学）
• The Hong Kong Polytechnic University（香港理工大学）

机构由 AI 辅助整理，请以论文原文为准。