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Journal article
Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges
Future internet, Vol.17(9), p.396
08/30/2025
Web of Science ID: WOS:001580972800001
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.
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- Title
- Reinforcement Learning in Medical Imaging
- Publication Details
- Future internet, Vol.17(9), p.396
- Resource Type
- Journal article
- Publisher
- MDPI
- Number of pages
- 28
- Grant note
- National Science Foundation: 2433800, 1946442, 2100134 National Science Foundation under NSF: 5R42LM014356-03 NIH
The work is supported by the National Science Foundation under NSF Grant #2433800, #1946442, #2100134, and NIH Grant #5R42LM014356-03. Any opinions, findings, recommendations, expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- Copyright
- © the authors
- Identifiers
- WOS:001580972800001; 99381483999206600
- Academic Unit
- Center for Cybersecurity and AI
- Language
- English