arXiv 2506.023272025-06-02cs.CV

医学世界模型：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

The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Johns Hopkins University（约翰霍普金斯大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；University of California, San Francisco（加州大学旧金山分校）

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

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出医学世界模型 MeWM，结合视觉-语言策略模型与肿瘤生成动力学模型模拟治疗后肿瘤演化，并通过逆动力学模型优化个体化治疗方案，将最优 TACE 方案选择的 F1 分数提升 13%。

摘要：

提供有效治疗并做出明智的临床决策是现代医学和临床护理的核心目标。我们关注利用大型生成模型的最新进展来模拟疾病动态，以辅助临床决策。为此，我们提出医学世界模型（MeWM），这是医学领域首个能够基于临床决策视觉化预测未来疾病状态的世界模型。MeWM 由两部分组成：（i）作为策略模型的视觉-语言模型，以及（ii）作为动力学模型的肿瘤生成模型。策略模型生成临床治疗等行动计划，而动力学模型在给定治疗条件下模拟肿瘤的进展或消退。在此基础上，我们提出逆动力学模型，对模拟的治疗后肿瘤应用生存分析，从而能够评估治疗效果并选择最优临床行动计划。结果表明，所提出的 MeWM 通过合成治疗后肿瘤来模拟疾病动态，在由放射科医生评估的图灵测试中达到了最先进的区分度。同时，其逆动力学模型在优化个体化治疗方案方面，在所有指标上均优于医学专用 GPT。值得注意的是，MeWM 改善了介入医生的临床决策，将选择最优 TACE 方案的 F1 分数提升了 13%，为未来将医学世界模型作为第二阅片者进行整合铺平了道路。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。