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Area of Science:

  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Prevention
  • Clinical Informatics

Background:

  • Large language models (LLMs) are increasingly used in healthcare.
  • Their specific role in cardiovascular (CV) prevention requires clarification.
  • This review examines LLM applications in preventive cardiology.

Purpose of the Study:

  • To synthesize evidence on LLM applications in preventive cardiology.
  • To propose a governance framework for safe LLM implementation.
  • To assess LLMs' potential and limitations in CV prevention.

Main Methods:

  • Comprehensive narrative review of literature (January 2015 - November 2025).
  • Synthesis across patient, clinician, and system application domains.
  • Evaluation of evidence for health literacy, decision support, and data management.

Main Results:

  • LLMs provide empathetic patient education but lack nuance for unsupervised advice.
  • Clinician support includes note summarization and documentation drafting; risk calculation is unreliable.
  • System applications show potential for phenotyping and risk prediction but face challenges like hallucinations and data privacy.

Conclusions:

  • LLMs can address structural barriers in CV prevention.
  • Current deployment should be as supervised reasoning engines, augmenting clinician judgment.
  • The C.A.R.D.I.O. framework is proposed for responsible LLM integration into clinical practice.