Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges

A. B. M. Kamrul Islam Riad, Md. Abdul Barek, Hossain Shahriar (), Guillermo Francia () and Sheikh Iqbal Ahamed
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A. B. M. Kamrul Islam Riad: Department of Intelligent Systems and Robotics, University of West Florida, Pensacola, FL 32514, USA
Md. Abdul Barek: Department of Intelligent Systems and Robotics, University of West Florida, Pensacola, FL 32514, USA
Hossain Shahriar: Center for CyberSecurity, University of West Florida, Pensacola, FL 32514, USA
Guillermo Francia: Center for CyberSecurity, University of West Florida, Pensacola, FL 32514, USA
Sheikh Iqbal Ahamed: Department of Computer Science, Marquette University, Milwaukee, WI 53233, USA

Future Internet, 2025, vol. 17, issue 9, 1-28

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.

Keywords: reinforcement learning; medical imaging; clinical AI; taxonomy; decision-making; interpretability; Large Language Models; privacy-preserving AI; data efficiency; Trustworthy AI; healthcare systems (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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