Year: 2025
Type: article
Abstract: Reinforcement learning (RL) is being used more in medical imaging for segmentation, detection, registration, and classification. This survey provides a comprehensive overview of RL techniques applied in this domain, categorizing the literature based on clinical task, imaging modality, learning paradigm, and algorithmic design. We introduce a unified taxonomy that supports reproducibility, highlights design guidance, and identifies underexplored intersections. Furthermore, we examine the integration of Large Language Models (LLMs) for automation and interpretability, and discuss privacy-preserving extensions using Differential Privacy (DP) and Federated Learning (FL). Finally, we address deployment challenges and outline future research directions toward trustworthy and scalable medical RL systems.
Source: Future Internet
DOI: 10.3390/fi17090396
Authors: A. B. M. Kamrul Islam Riad, Md. Abdul Barek, Hossain Shahriar, Guillermo Francia, Sheikh Iqbal Ahamed
Institutions: University of West Florida, Marquette University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,930
Awards: Argo Cyber Emerging Scholars (ACES): Developing a Cybersecurity Community of Practice, Collaborative Research: SaTC: EDU: Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware, A Framework for mHealth App Security and Privacy Analysis, Collaborative Research: SaTC: EDU: Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware
OpenAlex ID: W4413922470