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Inaccurate information regarding cardiovascular disease prevention enabled by generative artificial intelligence.

Astefanos Al-Dalakta1, Bianca Honnekeri1, Fatima Rodriguez2

  • 1Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA.

American Journal of Preventive Cardiology
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI models can easily produce inaccurate cardiovascular disease (CVD) prevention information. This study found both OpenAI and DeepSeek models generated unreliable health advice, highlighting risks of AI-generated medical content.

Keywords:
Artificial intelligenceCardiovascular diseasePrevention

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Prevention

Background:

  • Prevalence of inaccurate cardiovascular disease (CVD) prevention information online.
  • Increasing use of artificial intelligence (AI) chatbots for medical queries.
  • Potential impact of misinformation on public health decisions.

Purpose of the Study:

  • To evaluate the accuracy of CVD prevention information generated by two leading generative AI (genAI) models.
  • To assess genAI performance on common CVD prevention topics like statin therapy and cholesterol management.
  • To compare responses from OpenAI and DeepSeek models under neutral and inaccuracy-prompting conditions.

Main Methods:

  • Physician-led experiment involving two board-certified preventive cardiologists.
  • Evaluation of genAI responses to nine common CVD prevention topics using neutral and inaccuracy-tone prompts.
  • Grading of responses as appropriate, borderline, or inappropriate based on content and references.

Main Results:

  • OpenAI: 88.9% appropriate for neutral prompts; 0% appropriate (77.8% inappropriate) for inaccuracy prompts.
  • DeepSeek-R1: 66.7% appropriate for neutral prompts; 100% inappropriate for inaccuracy prompts.
  • Both models demonstrated a propensity to generate inaccurate CVD prevention information when prompted.

Conclusions:

  • Generative AI models can be easily prompted to produce inaccurate CVD prevention information.
  • Significant risks associated with AI-driven health information require further research and policy interventions.
  • Need for vigilance and critical evaluation of AI-generated medical content for public health safety.