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September 10, 2025Cardiology in Review

Artificial Intelligence in Predicting Sudden Cardiac Death

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Authors

HTHadrian Hoang-Vu TranATAudrey ThuATAnu Radha Twayana

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Overview

This review demonstrates AI models improve risk stratification in sudden cardiac death, highlighting validation challenges.

Key Points

  • AI algorithms trained on electrocardiograms can identify significant preclinical features linked to arrhythmias, enhancing predictive accuracy.
  • Dynamic AI models show promise in both long-term risk assessment and real-time detection of arrhythmias, offering a potential clinical advancement.
  • The integration of various data modalities can significantly improve the performance of predictive models for sudden cardiac death.
  • There are substantial challenges to the clinical adoption of AI, including validation, interpretability, and integration issues within existing systems.

Cite This Study

Tran et al. (2025) studied this question.

synapsesocial.com/papers/68c23e4fb210217d64791b17https://doi.org/10.1097/crd.0000000000001014
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction2026
  2. 2Artificial Intelligence in the Early Detection of Heart Disease2026
  3. 3Artificial Intelligence in the Early Detection of Heart Disease2026
  4. 4Integrating AI in Cardiovascular Systems: Innovations in Diagnosis, RiskPrediction, and Management2026
  5. 5Artificial intelligence-based ascertainment of sudden cardiac death in electronic health records: development, validation and utility across national datasets2026