# Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging

arXiv: https://arxiv.org/abs/2503.16543 · HTML: https://mlsanity.com/paper/2503.16543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 18 of 18)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments](https://mlsanity.com/paper/2005.10619/) (2020)
- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2108.11510/) (2021)
- [Deep Multiagent Reinforcement Learning: Challenges and Directions](https://mlsanity.com/paper/2106.15691/) (2021)
- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Explainable Deep Learning Methods in Medical Image Classification: A Survey](https://mlsanity.com/paper/2205.04766/) (2022)
- [Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives](https://mlsanity.com/paper/2307.05545/) (2023)
- [Medical Image Registration Using Deep Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2002.03401/) (2020)
- [Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives](https://mlsanity.com/paper/2401.02376/) (2024)
- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Image quality assessment for machine learning tasks using meta-reinforcement learning](https://mlsanity.com/paper/2203.14258/) (2022)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2105.10626/) (2021)
- [Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer](https://mlsanity.com/paper/2501.10924/) (2025)
- [UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2502.15713/) (2025)
- [Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior](https://mlsanity.com/paper/2301.05392/) (2023)
- [Blockchain-based Crowdsourced Deep Reinforcement Learning as a Service](https://mlsanity.com/paper/2501.16369/) (2025)

## Built on by (most cited first, 2 of 2)

- [End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning](https://mlsanity.com/paper/2511.00114/) (2025)
- [Learning-based Hierarchical Tracheal Anatomy Understanding from Sparse Surgical Demonstration Annotations for Ultrasound Robots](https://mlsanity.com/paper/2607.22789/) (2026)

## Abstract (arXiv)

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents that are capable of executing complex tasks through rewarded interactions with their environments. Existing surveys on advancements in the US scanning domain predominantly focus on partially autonomous solutions leveraging AI for scanning guidance, organ identification, plane recognition, and diagnosis. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this review proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline. This taxonomy not only highlights recent RL advancements in the US domain but also identifies unresolved challenges crucial for achieving fully autonomous US systems. This work aims to offer a thorough review of current research efforts, highlighting the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.