ReviewCardio-oncology (London, England)2026
From data to decision: a clinical pipeline for wearable AI in early cardiotoxicity detection.
Review in Cardio-oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.