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.
View on arXiv@article{elmekki2025_2503.16543,
title={ Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging },
author={ Hanae Elmekki and Saidul Islam and Ahmed Alagha and Hani Sami and Amanda Spilkin and Ehsan Zakeri and Antonela Mariel Zanuttini and Jamal Bentahar and Lyes Kadem and Wen-Fang Xie and Philippe Pibarot and Rabeb Mizouni and Hadi Otrok and Shakti Singh and Azzam Mourad },
journal={arXiv preprint arXiv:2503.16543},
year={ 2025 }
}