mlsanityData to 7 Oct 2026

Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey

arXiv 2108.04087 ↗First posted 5 Aug 2021cs.LG cs.AI cs.MAcited by 3

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Abstract (arXiv)

The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multi-agent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 52 of its 204 references that are in the map. First posted on arXiv 5 Aug 2021.

Builds on

52 of 204 references.

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38 more
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50 most cited of 52 All 52 (JSON)

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

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  3. 2023A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollupscited by 0