CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space

TL;DR

CLARITY is a novel medical world model that predicts disease progression in a structured latent space, incorporating patient-specific and temporal contexts to generate interpretable treatment plans and improve decision-making in oncology.

Contribution

The paper introduces CLARITY, a structured latent space world model that explicitly models treatment-conditioned disease trajectories with a feedback mechanism for treatment decisions.

Findings

Outperforms recent MeWM by 12% on MU-Glioma-Post dataset.

Surpasses all other medical-specific large language models.

Demonstrates state-of-the-art performance in treatment planning.

Abstract

Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform. While world models (WMs) offer a paradigm for generative prediction, existing medical applications remain limited. Existing methods often rely on stochastic diffusion models, focusing on visual reconstruction rather than causal, physiological transitions. Furthermore, in medical domain, models like MeWM typically ignore patient-specific temporal and clinical contexts and lack a feedback mechanism to link predictions to treatment decisions. To address these gaps, we introduce CLARITY, a medical world model that forecasts disease evolution directly within a structured latent space. It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory, and thus generate physiologically faithful, individualized treatment plans. Finally, CLARITY introduces a novel prediction-to-decision framework, translating latent rollouts into transparent, actionable recommendations. CLARITY demonstrates state-of-the-art performance in treatment planning. On the MU-Glioma-Post dataset, our approach outperforms recent MeWM by 12\%, and significantly surpasses all other medical-specific large language models.