医疗世界模型：面向治疗规划的肿瘤演化生成式模拟

Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning

June 2, 2025

作者: Yijun Yang, Zhao-Yang Wang, Qiuping Liu, Shuwen Sun, Kang Wang, Rama Chellappa, Zongwei Zhou, Alan Yuille, Lei Zhu, Yu-Dong Zhang, Jieneng Chen

cs.AI

摘要

在现代医学与临床护理中，提供有效治疗并做出明智的临床决策是核心目标。我们致力于利用大型生成模型的最新进展，模拟疾病动态以辅助临床决策。为此，我们引入了医学世界模型（Medical World Model, MeWM），这是医学领域首个基于临床决策视觉预测未来疾病状态的世界模型。MeWM由两部分构成：(i) 作为策略模型的视觉-语言模型，以及(ii) 作为动态模型的肿瘤生成模型。策略模型负责生成如临床治疗方案等行动计划，而动态模型则模拟在给定治疗条件下肿瘤的进展或消退情况。在此基础上，我们提出了逆向动态模型，该模型对模拟的治疗后肿瘤进行生存分析，从而评估治疗效果并筛选最优临床行动计划。因此，MeWM通过合成治疗后肿瘤来模拟疾病动态，在放射科医生评估的图灵测试中展现出顶尖的特异性。同时，其逆向动态模型在优化个体化治疗方案方面，各项指标均超越医疗专用GPT模型。尤为突出的是，MeWM显著提升了介入医生的临床决策能力，在选择最佳TACE方案时的F1分数提高了13%，为未来医学世界模型作为第二阅片者的整合铺平了道路。

English

Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.