Clinical Applications of AI in Post-Cancer Rehabilitation

Abstract

The article examines the potential of Artificial Intelligence (AI) and machine learning in oncology rehabilitation. Traditional rehabilitation models have limitations in delivering personalized care in real-time. AI technologies close these gaps by utilizing advanced predictive capabilities and optimizing treatment strategies. Convolutional Neural Networks (CNNs) in radiomics provide a proactive approach to managing conditions such as lymphedema. In the field of physical rehabilitation, the integration of robotic systems with AI algorithms allows for real-time adaptive control mechanisms. This integration results in optimized muscle fiber recruitment and improves functional outcomes. Moreover, AI-powered platforms provide individualized psychological and nutritional assistance, enhancing the comprehensive care of individuals who have survived cancer. Despite the promising advancements, ethical considerations, including data privacy and algorithmic bias, necessitate a multidisciplinary approach for responsible implementation. Computational limitations, such as the requirement for extensive labeled datasets, present additional challenges. The analysis highlights the necessity of additional research to validate these emerging technologies, overcome their limitations, and establish ethical frameworks for their responsible clinical implementation.

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