Reinforcement learning for intelligent healthcare applications: A survey
Abstract
Discovering new treatments and personalizing existing ones is one of the major goals of modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the realization of advanced intelligent systems able to learn about clinical treatments and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning (RL), which is a branch of Machine Learning (ML), has received significant attention in the medical community since it has the potentiality to support the development of personalized treatments in accordance with the more general precision medicine vision. This report presents a review of the role of RL in healthcare by investigating past work, and highlighting any limitations and possible future contributions.
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Reinforcement learning has been utilized to develop dynamic treatment regimens and provide a precise insulin dosage to react to the immediate needs of patients with diabetes.
“It has been utilized to develop dynamic treatment regimens and provide a precise insulin dosage to react to the immediate needs of patients with diabetes.23”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [23]
Analysis of medical data can fine-tune drug dosages for individual patients based on their characteristics and past responses.
“Analysis of medical data can fine-tune drug dosages for individual patients based on their characteristics and past responses [33].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [33]
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- Authors
- Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G.
- Affiliations
- National Research Council (IT)
- Parthenope University of Naples (IT)
- Corresponding author
- Coronato, Antonio
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 109
- Pages
- 101964
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W3090832565
- Cited by
- 320
- References
- 204
- Citations by year
- 2026: 43 · 2025: 67 · 2024: 55 · 2023: 73 · 2022: 51 · 2021: 28 · 2020: 3
- FWCI
- 11.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- reinforcement learning, medical applications, personalized treatment, precision medicine, clinical decision support, medical knowledge discovery
- MeSH terms
- Machine Learning, Artificial Intelligence, Delivery of Health Care, Reinforcement, Psychology, Precision Medicine
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