  The goal of precision medicine is to provide individualized treatment at each
stage of chronic diseases, a concept formalized by Dynamic Treatment Regimes
(DTR). These regimes adapt treatment strategies based on decision rules learned
from clinical data to enhance therapeutic effectiveness. Reinforcement Learning
(RL) algorithms allow to determine these decision rules conditioned by
individual patient data and their medical history. The integration of medical
expertise into these models makes possible to increase confidence in treatment
recommendations and facilitate the adoption of this approach by healthcare
professionals and patients. In this work, we examine the mathematical
foundations of RL, contextualize its application in the field of DTR, and
present an overview of methods to improve its effectiveness by integrating
medical expertise.
