Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare

Meiyi Feng

2024

Abstract

Reinforcement Learning (RL) has become a groundbreaking approach in machine learning, significantly impacting healthcare by providing solutions to intricate decision-making challenges. This comprehensive review examines the current state of RL in healthcare, focusing on dynamic treatment protocols, automated diagnostic systems, resource allocation, as well as privacy and security issues. RL's ability to adapt and optimize treatment plans dynamically, enhance diagnostic accuracy, and manage healthcare resources efficiently underscores its potential to revolutionize clinical practices. However, the implementation of RL in healthcare is fraught with challenges, including the need for extensive, high-quality datasets, difficulties in interpreting complex models, and significant data privacy concerns. To mitigate these challenges, recent innovations have been introduced. Transitional Variational Autoencoders (tVAEs) are used to generate realistic patient data, enhancing the simulation capabilities of RL models. Federated learning frameworks have been developed to ensure data privacy by enabling collaborative model training without sharing raw data. Additionally, transfer learning and domain adaptation techniques improve the generalization of RL models across diverse healthcare settings. This review provides a thorough analysis of these advancements and their implications for healthcare, offering a detailed understanding of RL's current applications and limitations. Future research directions are proposed to address existing challenges, aiming to ensure the robust, transparent, and ethical integration of RL technologies into clinical settings, thereby maximizing their potential to improve healthcare outcomes.

Paper Citation

in Harvard Style

Feng M. (2024). Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 39-44. DOI: 10.5220/0013205700004568

in Bibtex Style

@conference{ecai24,
author={Meiyi Feng},
title={Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={39-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013205700004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare
SN - 978-989-758-726-9
AU - Feng M.
PY - 2024
SP - 39
EP - 44
DO - 10.5220/0013205700004568
PB - SciTePress