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

In 30 seconds

This systematic review examined 20 peer-reviewed studies published between 2019 and 2023 on reinforcement learning (RL) for medical image analysis. The studies covered landmark detection, segmentation, lesion identification, diagnosis, registration, and related tasks, using classical and deep RL methods. Reported applications included DQN for landmark detection and cardiovascular risk estimation, and PPO and A2C for vessel tracking. The authors propose a four-component framework for RL in medical imaging and identify persistent challenges in policy optimization, anatomical context, data availability, and interpretability. The review describes promising applications but does not report a pooled quantitative estimate of performance.

Key findings

• The review synthesized 20 studies published between 2019 and 2023 across multiple medical imaging tasks.
• DQN was reported to perform strongly in anatomical landmark detection and cardiovascular risk estimation.
• PPO and A2C achieved optimal policy learning for vessel tracking in the reviewed studies.
• The proposed RLMI framework comprises state representation, policy optimization, reward formulation, and environment modeling.

Why it matters

RL offers a way to model sequential decisions in image-analysis tasks, including navigation, detection, and registration. The review organizes diverse approaches into a common framework and highlights barriers relevant to translation, particularly data scarcity and limited interpretability. These findings may help guide evaluation and development, while the breadth of applications should not be mistaken for established clinical effectiveness.

What to keep in mind

The review includes only 20 studies published between 2019 and 2023, and the abstract provides no pooled quantitative performance estimate or evidence of clinical deployment outcomes.