Advancements in reinforcement learning for clinical decision-making in healthcare: a systematic review
TL;DR
Overall, RL-based CDSS are still partially autonomous, they often prioritize autonomy and personalization over runtime oversight, interpretability, and evaluation, and an “agentic readiness”framework is suggested to address these gaps.
Abstract
Clinical decision-making increasingly relies on data-driven tools, but most systems today are still predictive models that work at isolated time points. Reinforcement learning (RL) provides a different approach by optimizing sequences of actions under uncertainty. It’s often seen as a foundation for more “agentic”AI in healthcare. We conducted a systematic literature review of RL-based clinical decision support systems (CDSS) published between 2020 and January 2026. We reviewed 66 studies, looking at the clinical domain, decision type, RL methods, data, and system maturity. RL-based CDSS are mostly used in critical care, cardiology, oncology, and diabetes, focusing on therapeutic dosing optimization. Actor-critic and policy-gradient methods are mainly used in continuous physiological/device-control settings. Most systems are trained offline using historical data: 66.7% rely on observational clinical data, 21.2% use simulated environments, and 12.1% combine both. Overall, RL-based CDSS are still partially autonomous, they often prioritize autonomy and personalization over runtime oversight, interpretability, and evaluation. We suggest an “agentic readiness”framework to address these gaps and emphasize the need for better safeguards, clearer reporting, and more human-centered assessments.