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

Author

Listed:
  • 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)

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.

Suggested Citation

  • A. B. M. Kamrul Islam Riad & Md. Abdul Barek & Hossain Shahriar & Guillermo Francia & Sheikh Iqbal Ahamed, 2025. "Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges," Future Internet, MDPI, vol. 17(9), pages 1-28, August.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:396-:d:1738221
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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