From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
The article discusses a new approach to clinical prediction that moves from static risk assessments to dynamic modeling of disease trajectories. It emphasizes the importance of intervention-aware models that account for treatment effects and patient-specific disease evolution. The authors propose a unified framework that integrates various modeling techniques to improve clinical decision-making.
- ▪Static prediction models often fail in clinical settings due to conflating disease biology with clinician behavior.
- ▪The review focuses on intervention-aware disease trajectory modeling in clinical AI, which estimates patient-specific longitudinal disease evolution.
- ▪The authors present a unified framework that bridges forecasting, counterfactual trajectories, and policy evaluation.
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Computer Science > Artificial Intelligence arXiv:2605.16927 (cs) [Submitted on 16 May 2026] Title:From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction Authors:Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari, Min Hun Lee, Siew-Kei Lam, Erik Cambria, Xibin Sun, Yangtao Zhou, Tong Yang, Xiaoyu Zhang, Tao Tan, Yue Sun, Bin Cui View a PDF of the paper titled From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction, by Pujun Feng and 12 other authors View PDF Abstract:Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices.
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