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

Islam Riad, A. B. M. Kamrul; Barek, Md. Abdul; Shahriar, Hossain; Francia, Guillermo; Ahamed, Sheikh Iqbal

doi:10.3390/fi17090396

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

by

A. B. M. Kamrul Islam Riad

¹

,

Md. Abdul Barek

¹

,

Hossain Shahriar

^2,*

,

Guillermo Francia III

²

and

Sheikh Iqbal Ahamed

³

¹

Department of Intelligent Systems and Robotics, University of West Florida, Pensacola, FL 32514, USA

²

Center for CyberSecurity, University of West Florida, Pensacola, FL 32514, USA

³

Department of Computer Science, Marquette University, Milwaukee, WI 53233, USA

^*

Author to whom correspondence should be addressed.

Future Internet 2025, 17(9), 396; https://doi.org/10.3390/fi17090396 (registering DOI)

Submission received: 24 July 2025 / Revised: 22 August 2025 / Accepted: 25 August 2025 / Published: 30 August 2025

(This article belongs to the Special Issue Artificial Intelligence for Smart Healthcare: Methods, Applications, and Challenges)

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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

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MDPI and ACS Style

Islam Riad, A.B.M.K.; Barek, M.A.; Shahriar, H.; Francia, G., III; Ahamed, S.I. Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges. Future Internet 2025, 17, 396. https://doi.org/10.3390/fi17090396

AMA Style

Islam Riad ABMK, Barek MA, Shahriar H, Francia G III, Ahamed SI. Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges. Future Internet. 2025; 17(9):396. https://doi.org/10.3390/fi17090396

Chicago/Turabian Style

Islam Riad, A. B. M. Kamrul, Md. Abdul Barek, Hossain Shahriar, Guillermo Francia, III, and Sheikh Iqbal Ahamed. 2025. "Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges" Future Internet 17, no. 9: 396. https://doi.org/10.3390/fi17090396

APA Style

Islam Riad, A. B. M. K., Barek, M. A., Shahriar, H., Francia, G., III, & Ahamed, S. I. (2025). Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges. Future Internet, 17(9), 396. https://doi.org/10.3390/fi17090396

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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