Guidelines as Control Logic for Multimodal Sequential Clinical Decision-Making

Real-world clinical decision-making is dynamic, requiring clinicians to continuously acquire patient information and make sequential decisions according to Clinical Practice Guidelines (CPGs). Existing approaches typically incorporate CPG knowledge in two forms: external or parametric. External approaches commonly rely on retrieval-augmented generation (RAG), which injects large amounts of retrieved CPGs into the model context, following a “Guideline as Context” paradigm. Parametric approaches encode guideline knowledge into model parameters, yet still fail to transform guidelines into explicit, traceable, and easily updatable decision control logic. To address these limitations, we propose CPGAgent, a guideline-grounded framework that introduces a “Guidelines as Control Logic” paradigm for multimodal sequential clinical decision-making. Our main idea is to encode CPGs as latent guideline memory slots and dynamically activate relevant slots according to evolving patient states, enabling guideline-guided trajectory planning. For evaluating dynamic decision trajectories, we introduce CPGTrajBench, a multimodal executable benchmark in which evidence is progressively revealed through agent actions. We further develop a guideline-grounded evaluation framework with trajectory-planning rubrics, assessing guideline-consistent planning across the entire clinical trajectory. Experiments show that CPGAgent achieves the best trajectory performance across three cancer types, matching the strongest baseline on one cohort and outperforming it by 9.6 and 9.2 points on the other two. Anonymized code is provided in the reproducibility statement.