Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes. Although current medical AI can detect disease, estimate risk and generate reports, many systems still return static labels or scores, offering limited insight into how illness may progress or how alternative interventions may reshape its trajectory. Medical world models adapt the world-model idea from artificial intelligence to healthcare by learning internal simulators of patient-state dynamics. Their long-term goal is to help clinicians anticipate deterioration, compare treatment-conditioned futures and tailor care to individual patients. Yet relevant work remains scattered across foundation models, longitudinal modelling, disease simulation, treatment-effect estimation, reinforcement learning and digital twins. To bridge this gap, this review outlines a roadmap for advancing medical AI from isolated diagnosis and prediction toward medical world models that simulate disease evolution and support intervention decisions. This roadmap is organized around three coupled capabilities: patient-state construction, clinical dynamics modelling and intervention decision support. Across representative systems, the comparison highlights what each capability contributes and how partial components can be integrated into more mature perception--dynamics--planning systems. Finally, we identify the challenges involved in turning plausible rollouts into clinically useful simulators. Related literature is available at https://github.com/1999kevin/awesome_medical_world_models.

翻译：医学诊断和治疗是动态过程，患者状态随时间演变，临床干预改变未来结局。尽管当前医学人工智能能够检测疾病、评估风险并生成报告，但许多系统仍仅输出静态标签或评分，对疾病如何进展、或替代干预如何重塑其轨迹提供的洞察有限。医学世界模型将人工智能中的世界模型理念适配至医疗领域，通过学习患者状态动态的内部模拟器。其长期目标是帮助临床医生预测病情恶化、比较基于不同治疗条件下的未来结局，并为个体患者量身定制护理方案。然而，相关研究仍分散于基础模型、纵向建模、疾病模拟、治疗效果估计、强化学习及数字孪生等方向。为弥合这一缺口，本综述勾勒了将医学人工智能从孤立诊断与预测推进至模拟疾病演变并支持干预决策的医学世界模型的路线图。该路线图围绕三个耦合能力组织：患者状态构建、临床动态建模与干预决策支持。通过代表性系统的比较，突出了每项能力的贡献，以及如何将部分组件整合为更成熟的“感知-动态-规划”系统。最后，我们指出了将看似可行的推演转化为临床实用模拟器所面临的挑战。相关文献参见 https://github.com/1999kevin/awesome_medical_world_models。