CLARITY：用于通过在潜在空间中建模情境感知疾病轨迹来指导治疗决策的医学世界模型
CLARITY: Medical World Model for Guiding Treatment Decisions by Simulating Context-Aware Disease Trajectories
AI总结：
CLARITY通过在潜在空间中建模时间间隔和患者特定数据，预测疾病演变轨迹，生成个性化治疗方案，并在医学领域实现最先进的治疗规划性能。
摘要：
癌症临床决策需要预测动态疾病演变，而当前静态AI预测器无法完成此任务。虽然世界模型（WMs）提供了一种生成性预测范式，但现有医学应用仍有限。现有方法通常依赖于随机扩散模型，专注于视觉重建而非因果、生理过渡。此外，在医学领域，像MeWM这样的模型通常忽略患者特定的时间和临床上下文，并缺乏将预测与治疗决策联系起来的反馈机制。为解决这些差距，我们引入了CLARITY，一种医学世界模型，它直接在结构化潜在空间中预测疾病演变。它明确整合时间间隔（时间上下文）和患者特定数据（临床上下文），以建模治疗条件下的进展作为平滑、可解释的轨迹，从而生成生理上忠实的、个性化的治疗计划。最后，CLARITY引入了一种新的预测到决策框架，将潜在滚动转化为透明、可操作的推荐。CLARITY在治疗规划中展示了最先进的性能。在MU-Glioma-Post数据集上，我们的方法比最近的MeWM高出12%，并且显著超过了所有其他医学专用的大语言模型。
英文摘要：
Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that cannot model longitudinal, treatment-conditioned progression. Although generative and world models have demonstrated strong capabilities in general domains, their adaptation to medicine remains limited and insufficient for capturing complex, treatment-induced physiological dynamics across temporal scales. To address these gaps, we introduce CLARITY, a medical world model that enables counterfactual simulation of treatment-conditioned disease trajectories for clinical decision-making. By jointly encoding imaging-derived latent states, temporal intervals that capture irregular follow-ups, and patient-specific clinical context, CLARITY learns smooth and interpretable representations of disease progression, allowing the model to simulate how alternative treatments reshape future disease dynamics. Because treatment optimization is inherently sequential and uncertain, requiring evaluation of long-term outcomes across multiple possible interventions, we further propose an entropy-regularized, computationally efficient long-horizon prediction-to-decision framework that plans treatment strategies over imagined disease trajectories and iteratively refines therapy proposals through survival-aware feedback, forming a closed-loop simulation-to-decision framework for treatment planning. CLARITY achieves state-of-the-art performance in treatment planning and survival prediction across three cancer datasets, including two brain tumor cohorts (MU-Glioma-Post and zero-shot on UCSF-ALPTDG) and one breast cancer dataset (ISPY-2), demonstrating strong generalization across cancer types while consistently outperforming prior generative methods and medical-domain large language model baselines.
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。
