醫學世界模型：腫瘤演化的生成模擬用於治療規劃

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

提供有效治療並做出明智的臨床決策，是現代醫學與臨床照護的核心目標。我們致力於模擬疾病動態以輔助臨床決策，並充分利用大型生成模型的最新進展。為此，我們引入了醫學世界模型（Medical World Model, MeWM），這是首個在醫學領域中基於臨床決策視覺化預測未來疾病狀態的世界模型。MeWM由兩部分組成：(i) 作為策略模型的視覺-語言模型，以及(ii) 作為動態模型的腫瘤生成模型。策略模型生成如臨床治療的行動計劃，而動態模型則模擬在特定治療條件下腫瘤的進展或消退。基於此，我們提出了逆動態模型，該模型將生存分析應用於模擬的治療後腫瘤，從而評估治療效果並選擇最佳的臨床行動方案。結果顯示，所提出的MeWM通過合成治療後腫瘤來模擬疾病動態，在放射科醫生評估的圖靈測試中展現了頂尖的特異性。同時，其逆動態模型在優化個體化治療方案的所有指標上均優於醫學專用GPT。值得注意的是，MeWM提升了介入醫師的臨床決策能力，在選擇最佳TACE方案時的F1分數提高了13%，為未來醫學世界模型作為第二讀者的整合鋪平了道路。

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.