Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes

Frommeyer, Timothy C.; Gilbert, Michael M.; Fursmidt, Reid M.; Park, Youngjun; Khouzam, John Paul; Brittain, Garrett V.; Frommeyer, Daniel P.; Bett, Ean S.; Bihl, Trevor J.

doi:10.3390/healthcare13141752

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Open AccessSystematic Review

Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes

by

Timothy C. Frommeyer

^1,*

,

Michael M. Gilbert

²,

Reid M. Fursmidt

³

,

Youngjun Park

^4,5,

John Paul Khouzam

⁴,

Garrett V. Brittain

¹,

Daniel P. Frommeyer

⁶,

Ean S. Bett

⁷ and

Trevor J. Bihl

⁵

¹

Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA

²

Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA

³

Northwell Health System, Department of Radiology, Zucker School of Medicine at Hofstra/Northwell Manhasset, New York, NY 11549, USA

⁴

Boonshoft School of Medicine, Wright State University, Dayton, OH 45324, USA

⁵

Department of Pharmacology & Toxicology, Boonshoft School of Medicine, Wright State University, Dayton, OH 45324, USA

⁶

Department of Biology, University of Dayton, Dayton, OH 45469, USA

⁷

Department of Family Medicine, Fairfield Medical Center, Fairfield, OH 43130, USA

^*

Author to whom correspondence should be addressed.

Healthcare 2025, 13(14), 1752; https://doi.org/10.3390/healthcare13141752 (registering DOI)

Submission received: 9 May 2025 / Revised: 11 July 2025 / Accepted: 17 July 2025 / Published: 19 July 2025

(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)

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Abstract

Background/Objectives: Reinforcement learning (RL), a subset of machine learning, has emerged as a promising tool for supporting precision medicine and dynamic treatment regimes by enabling adaptive, data-driven clinical decision making. Despite its potential, challenges such as interpretability, reward definition, data limitations, and clinician adoption remain. This review aims to evaluate the recent advancements in RL in precision medicine and dynamic treatment regimes, highlight clinical fields of application, and propose practical frameworks for future integration into medical practice. Methods: A systematic review was conducted following PRISMA guidelines across PubMed, MEDLINE, and Web of Science databases, focusing on studies from January 2014 to December 2024. Articles were included based on their relevance to RL applications in precision medicine and dynamic treatment regime within healthcare. Data extraction captured study characteristics, algorithms used, specialty area, and outcomes. Results: Forty-six studies met the inclusion criteria. RL applications were concentrated in endocrinology, critical care, oncology, and behavioral health, with a focus on dynamic and personalized treatment planning. Hybrid and value-based RL methods were the most utilized. Since 2020, there has been a sharp increase in RL research in healthcare, driven by advances in computational power, digital health technologies, and increased use of wearable devices. Conclusions: RL offers a powerful opportunity to augment clinical decision making by enabling dynamic and individualized patient care. Addressing key barriers related to transparency, data availability, and alignment with clinical workflows will be critical to translating RL into everyday medical practice.

Keywords: reinforcement learning; machine learning; artificial intelligence; precision medicine; dynamic treatment regimen; health informatics

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MDPI and ACS Style

Frommeyer, T.C.; Gilbert, M.M.; Fursmidt, R.M.; Park, Y.; Khouzam, J.P.; Brittain, G.V.; Frommeyer, D.P.; Bett, E.S.; Bihl, T.J. Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes. Healthcare 2025, 13, 1752. https://doi.org/10.3390/healthcare13141752

AMA Style

Frommeyer TC, Gilbert MM, Fursmidt RM, Park Y, Khouzam JP, Brittain GV, Frommeyer DP, Bett ES, Bihl TJ. Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes. Healthcare. 2025; 13(14):1752. https://doi.org/10.3390/healthcare13141752

Chicago/Turabian Style

Frommeyer, Timothy C., Michael M. Gilbert, Reid M. Fursmidt, Youngjun Park, John Paul Khouzam, Garrett V. Brittain, Daniel P. Frommeyer, Ean S. Bett, and Trevor J. Bihl. 2025. "Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes" Healthcare 13, no. 14: 1752. https://doi.org/10.3390/healthcare13141752

APA Style

Frommeyer, T. C., Gilbert, M. M., Fursmidt, R. M., Park, Y., Khouzam, J. P., Brittain, G. V., Frommeyer, D. P., Bett, E. S., & Bihl, T. J. (2025). Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes. Healthcare, 13(14), 1752. https://doi.org/10.3390/healthcare13141752

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Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes

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