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Reinforcement learning (RL) has emerged as a powerful approach for tacklingcomplex medical decision-making problems such as treatment planning,personalized medicine, and optimizing the scheduling of surgeries andappointments. It has gained significant attention in the field of NaturalLanguage Processing (NLP) due to its ability to learn optimal strategies fortasks such as dialogue systems, machine translation, and question-answering.This paper presents a review of the RL techniques in NLP, highlighting keyadvancements, challenges, and applications in healthcare. The review begins byvisualizing a roadmap of machine learning and its applications in healthcare.And then it explores the integration of RL with NLP tasks. We examined dialoguesystems where RL enables the learning of conversational strategies, RL-basedmachine translation models, question-answering systems, text summarization, andinformation extraction. Additionally, ethical considerations and biases inRL-NLP systems are addressed.

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare