AI-Assisted Exercise Prescription for Cardio-Oncology Preventive Care

Abstract

The intersection of cardiology and oncology has emerged as a critical medical discipline due to the profound cardiovascular complications associated with life-saving cancer therapies. Cardiotoxicity remains a primary cause of morbidity and mortality among cancer survivors, necessitating robust preventive strategies. While exercise is widely recognized as a potent countermeasure against treatment-induced cardiovascular decline, standardizing exercise prescriptions for a highly heterogeneous cardio-oncology population presents significant clinical challenges. This paper explores the development, implementation, and efficacy of artificial intelligence systems in generating personalized exercise prescriptions for cardio-oncology preventive care. By integrating high-dimensional clinical data, including electronic health records, genomic profiles, and real-time wearable sensor telemetry, machine learning algorithms can dynamically optimize the frequency, intensity, time, and type of exercise required to maximize cardioprotection while minimizing the risk of adverse events. Through a comprehensive review of current literature, detailed methodological frameworks, and analysis of predictive performance metrics, this research demonstrates that algorithmically tailored exercise interventions significantly outperform generic clinical guidelines in preserving cardiorespiratory fitness and enhancing patient adherence. The integration of artificial intelligence into cardio-oncology represents a paradigm shift from reactive treatment to precision preventive care, offering a scalable solution to mitigate the long-term cardiovascular burden of cancer survivorship.