# Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework

## TL;DR

This paper reviews how large language models can support cardiovascular prevention and proposes a governance framework to ensure their safe use in clinical settings.

## Contribution

The paper introduces the C.A.R.D.I.O. framework for responsible integration of LLMs in cardiovascular prevention.

## Key findings

- LLMs can improve patient education and clinical documentation but lack reliability for personalized advice and risk calculations.
- System applications of LLMs show promise in automated phenotyping and risk prediction but face challenges like hallucinations and data privacy.
- The C.A.R.D.I.O. framework is proposed to guide the safe deployment of LLMs in clinical practice.

## Abstract

Background: Large language models (LLMs) are becoming progressively integrated into clinical practice; however, their role in cardiovascular (CV) prevention remains unclear. This review synthesizes current evidence on LLM applications in preventive cardiology and proposes a governance framework for their safe translation into practice. Methods: We conducted a comprehensive narrative review of literature published between January 2015 and November 2025. Evidence was synthesized across three functional domains: (1) patient applications for health literacy and behavior change; (2) clinician applications for decision support and workflow efficiency; and (3) system applications for automated data extraction, registry construction, and quality surveillance. Results: Evidence suggests that while LLMs generate empathetic, guideline-concordant patient education, they lack the nuance required for unsupervised, personalized advice. For clinicians, LLMs effectively summarize clinical notes and draft documentation but remain unreliable for deterministic risk calculations and autonomous decision-making. System-facing applications demonstrate potential for automated phenotyping and multimodal risk prediction. However, safe deployment is constrained by hallucinations, temporal obsolescence, automation bias, and data privacy concerns. Conclusions: LLMs could help mitigate structural barriers in CV prevention but should presently be deployed only as supervised “reasoning engines” that augment, rather than replace, clinician judgment. To guide the transition from in silico performance to bedside practice, we propose the C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, and Ongoing vigilance) as a roadmap for responsible integration.