What question did this study set out to answer?

This review aims to summarize the applications and challenges of reinforcement learning in decision-making for sepsis treatment.

July 20, 2026Open Access

Reinforcement learning for treatment decision-making in sepsis: a scoping review

ZTZiyi TangZhongda Hospital Southeast University PSPeilin SunZhongda Hospital Southeast University RLRuitong LiangChengdu University of Technology

Key Points

This review aims to summarize the applications and challenges of reinforcement learning in decision-making for sepsis treatment.
Scoping review of 72 studies focused on reinforcement learning in sepsis management, primarily utilizing the MIMIC database (58 studies).
Analysis of study designs regarding state representation, action space, reward definition, and algorithms used.
Assessment of the focus areas in sepsis treatment including vasopressors, intravenous fluids, antibiotics, and other interventions.
Many reinforcement learning policies showed improved outcomes over clinician decisions, though their evaluation methods are often uncertain.
The majority of studies concentrated on vasopressor and fluid management with less focus on other therapies like antibiotics and corticosteroids.
Emphasis on the need for better evaluation tools and more diverse treatment strategies in future research.

Abstract

This scoping review summarizes the progress of reinforcement learning (RL) in clinical decision-making for sepsis at the intersection of medicine and artificial intelligence (AI). All 72 included studies were retrospective, with the majority using the Medical Information Mart for Intensive Care (MIMIC) database (58 studies 80.6%), and relatively few employing private datasets (10 studies 13.9%). Study designs varied widely, especially in state representation, action space, reward definition, and choice of algorithms. Most research focused on vasopressor and intravenous fluid management, while fewer studies addressed antibiotics, corticosteroids, mechanical ventilation, heparin, or vasopressin. Although many studies reported RL-derived policies that outperformed clinicians, the reliability and validity of the evaluation methods remain uncertain. Future research should emphasize clinically guided design of states, actions, and rewards, develop rigorous and widely accepted evaluation tools, and explore a broader range of sepsis treatment strategies. Ultimately, advancing interpretability, generalizability, and safety will be critical to effectively integrating RL into routine clinical practice.

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