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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02327 |
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| _version_ | 1866913872092332032 |
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| author | Yang, Yijun Wang, Zhao-Yang Liu, Qiuping Sun, Shuwen Wang, Kang Chellappa, Rama Zhou, Zongwei Yuille, Alan Zhu, Lei Zhang, Yu-Dong Chen, Jieneng |
| author_facet | Yang, Yijun Wang, Zhao-Yang Liu, Qiuping Sun, Shuwen Wang, Kang Chellappa, Rama Zhou, Zongwei Yuille, Alan Zhu, Lei Zhang, Yu-Dong Chen, Jieneng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning Yang, Yijun Wang, Zhao-Yang Liu, Qiuping Sun, Shuwen Wang, Kang Chellappa, Rama Zhou, Zongwei Yuille, Alan Zhu, Lei Zhang, Yu-Dong Chen, Jieneng Computer Vision and Pattern Recognition 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. |
| title | Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.02327 |