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A Primer on Reinforcement Learning in Medicine for Clinicians
Pushkala Jayaraman1, Jacob Desman1, Moein Sabounchi1
1The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Abstract:
Reinforcement Learning (RL) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. RL leverages patient-data to create personalized treatment plans, improving outcomes and resource efficiency. This review introduces RL to a clinical audience, exploring core concepts, potential applications, and challenges in integrating RL into clinical practice, offering insights into efficient, personalized, and effective patient care.
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