AI-Driven Physical Rehabilitation Strategies in Post-Cancer Care

Abstract

Artificial intelligence (AI) has made significant progress in addressing the specific obstacles related to post-cancer physical rehabilitation. This article examines AI technologies such as Support Vector Machines (SVM), Bayesian Inference, Reinforcement Learning, and Partially Observable Markov Decision Processes (POMDPs), focusing on their potential to improve the effectiveness and adaptability of rehabilitation strategies. SVM is recognized for its capability to analyze high-dimensional data obtained from wearable sensors, thereby enabling realtime patient monitoring. Bayesian methods facilitate the flexible adjustment of treatment plans, enhancing the efficient allocation of resources in healthcare environments. Reinforcement Learning enables realtime, dynamic optimization in robotic-assisted physiotherapy, yet it also raises ethical concerns regarding automated decision-making. POMDPs provide a mathematical framework for effectively addressing the uncertainties involved in post-cancer care. AI methods have significant potential for personalized and realtime adaptive treatments. However, it is important to address ethical considerations such as data privacy, informed consent, and algorithmic fairness through further investigation. This article emphasizes the importance of interdisciplinary research and ethical governance in maximizing the potential of AI in transforming post-cancer rehabilitation.