Continuous monitoring with wearable AI can predict chemotherapy-induced LVEF decline with moderate to high accuracy, though large-scale external validation is needed before clinical adoption.
Does continuous monitoring with wearable devices and AI improve early detection of subclinical chemotherapy-induced cardiotoxicity in cancer survivors?
A clinical pipeline utilizing wearable devices and AI for continuous monitoring shows promise as an early-warning system for chemotherapy-induced cardiotoxicity, though large-scale validation is required prior to clinical adoption.
Absolute Event Rate: 0% vs 0%
Abstract Chemotherapy-induced cardiotoxicity (CIC) is a leading cause of morbidity in cancer survivors, as conventional surveillance often detects cardiac dysfunction only after significant injury. This review moves beyond summarizing emerging technologies to focus on the end-to-end clinical pipeline—from sensor data to actionable decision-making—for creating a proactive “early-warning” system. We examine how continuous monitoring with wearable devices and artificial intelligence (AI) can detect subclinical CIC by analyzing digital biomarkers like heart rate variability. Proof-of-concept studies show AI can predict LVEF decline with moderate to high accuracy, though evidence is limited by small cohorts and lacks external validation. Implementing this wearable-AI pipeline could fill a critical surveillance gap and enable timely cardioprotective interventions. However, large-scale validation and building clinician trust through interpretable models are crucial before routine clinical adoption.
Zhou et al. (Mon,) reported a other. Continuous monitoring with wearable AI can predict chemotherapy-induced LVEF decline with moderate to high accuracy, though large-scale external validation is needed before clinical adoption.