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April 8, 2026Cardiology in Review

Artificial Intelligence–Enabled Prediction of Reversible Versus Irreversible Chemotherapy-Induced Myocardial Injury: Toward Precision Cardio-Oncology

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Key result

Artificial intelligence models show improved sensitivity over conventional surveillance for identifying subclinical chemotherapy-induced myocardial dysfunction and predicting its reversibility.

Authors

PAParth AdrejiyaNNNegarsadat NeshatAPAnsy Patel

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Overview

Artificial intelligence models integrating multimodal data show promise in differentiating reversible from irreversible chemotherapy-induced myocardial injury, though prospective validation is needed.

Key Points

  • To explore artificial intelligence applications for predicting reversible and irreversible myocardial injury caused by chemotherapy in cancer patients.
  • Review of contemporary AI techniques in cardio-oncology
  • Integration of clinical data from electronic health records and biomarkers
  • Evaluation of machine learning models for sensitivity in identifying myocardial dysfunction
  • Proposal of a framework for assessing myocardial injury reversibility
  • Machine learning models improve sensitivity for detecting subclinical myocardial dysfunction
  • Differentiation of transient myocardial injury from permanent fibrosis is enhanced
  • Highlights the need for further validation and development for clinical applicability

Cite This Study

Adrejiya et al. (2026) studied this question. Artificial intelligence models show improved sensitivity over conventional surveillance for identifying subclinical chemotherapy-induced myocardial dysfunction and predicting its reversibility.

synapsesocial.com/papers/69d5f09e74eaea4b11a7a050https://doi.org/10.1097/crd.0000000000001268
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