Explore our sister journals here.

Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 11, 2023
Date Accepted: Jan 31, 2024

The final, peer-reviewed published version of this preprint can be found here:

Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study

Mishra V, Sarraju A, Kalwani NM, Dexter JP

Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study

J Med Internet Res 2024;26:e55388

DOI: 10.2196/55388

PMID: 38648104

PMCID: 11074888

Evaluation of Prompts to Simplify Cardiovascular Disease Information Using a Large Language Model: Cross-Sectional Study

  • Vishala Mishra; 
  • Ashish Sarraju; 
  • Neil M. Kalwani; 
  • Joseph P. Dexter

ABSTRACT

In this cross-sectional study, we describe a human-in-the-loop prompt engineering strategy to produce simplified but complete cardiovascular disease prevention information using ChatGPT.


 Citation

Please cite as:

Mishra V, Sarraju A, Kalwani NM, Dexter JP

Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study

J Med Internet Res 2024;26:e55388

DOI: 10.2196/55388

PMID: 38648104

PMCID: 11074888

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.