Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model.

By Ashish Sarraju, Dennis Bruemmer, E. V. Van Iterson, L. Cho, Fatima Rodriguez, Luke Laffin. Journal of the American Medical Association (JAMA), 2023

382 citations recorded by the source corpus.

This study examines the appropriateness of artificial intelligence model responses to fundamental cardiovascular disease prevention questions.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Health Informatics

View Paper: https://www.semanticscholar.org/paper/22ff1f6f4df0497323ac03f446cbc49463128486

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Arun James Thirunavukarasu
2. Laura Gutierrez
3. Ting Fang Tan
4. K. Elangovan
5. D. Ting
6. Darren S. J. Ting

Citations in the corpus

• Large language models in medicine (2023, background): “ChatGPT has been shown to fail specialist examinations for doctors and provide inaccurate information in response to realistic patient queries regarding cardiovascular disease prevention 16,45 .”
• Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery (2026, unknown): “Data privacy, ethical considerations, and regulatory compliance also present significant barriers to the widespread adoption of these technologies in healthcare settings Meyer et al. [40], Milano et al. [41], Raza et al. [42], Zhou et al. [43, 44], Zeng et al. [45].”