Abstract
Telemedicine, particularly robotic-assisted healthcare, has grown in recent years, requiring robots to autonomously adapt to changing patient conditions, clinical tasks, and the environment. Adaptive reinforcement learning, a subfield of machine learning, can give robots the ability to dynamically adjust their behaviors based on feedback in the form of rewards or penalties. However, the design of reinforcement learning algorithms for telemedicine poses unique challenges, including safety, interpretability, and diversity of patient populations and clinical tasks. In this paper, we review the concept of adaptive reinforcement learning for telemedicine robotics, discussing the challenges, benefits, and current state of research. We also highlight future considerations and directions for the design of adaptive reinforcement learning algorithms that allow robots to learn and adapt autonomously in telemedicine to improve patient care.
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Boudjaj, M., Bakkali, F., Alidrissi, N., Jhilal, F., Bougdira, A. (2024). Adaptive Reinforcement Learning for Medical Robotics and Telemedicine. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 904. Springer, Cham. https://doi.org/10.1007/978-3-031-52388-5_38
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