Reinforcement learning for medical image analysis: a systematic review of algorithms, engineering challenges, and clinical deployment

Reinforcement learning (RL) has emerged as a powerful artificial intelligence paradigm in medical image analysis, excelling in complex decision-making tasks. This systematic review synthesizes the applications of RL across diverse imaging domains-including landmark detection, image segmentation, lesion identification, disease diagnosis, and image registration-by analyzing 20 peer-reviewed studies published between 2019 and 2023. RL methods are categorized into classical and deep reinforcement learning (DRL) approaches, focusing on their performance, integration with other machine learning models, and clinical utility. Deep Q-Networks (DQN) demonstrated strong performance in anatomical landmark detection and cardiovascular risk estimation, while Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) achieved optimal policy learning for vessel tracking. Policy gradient methods such as REINFORCE, Twin-Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC) were successfully applied to breast lesion detection, white-matter connectivity analysis, and vertebral segmentation.Monte Carlo learning, meta-RL, and A3C methods proved effective for adaptive questioning, image quality evaluation, and multimodal image registration. To consolidate these findings, we propose a unified Reinforcement Learning Medical Imaging (RLMI) framework encompassing four core components: state representation, policy optimization, reward formulation, and environment modeling. This framework enhances sequential agent learning, stabilizes navigation, and generalizes across imaging modalities and tasks. Key challenges remain, including optimizing task-specific policies, integrating anatomical contexts, addressing data scarcity, and improving interpretability. This review highlights RL's potential to enhance accuracy, adaptability, and efficiency in medical image analysis, providing valuable guidance for researchers and clinicians applying RL in real-world healthcare settings.