Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55388, first published .
Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study

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

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

Journals

  1. Mishra V, Blasi D, Dexter J. Bridging Ethics and Evidence: Language as a Critical Determinant of Health Equity. The American Journal of Bioethics 2024;24(11):66 View
  2. Aydin S, Karabacak M, Vlachos V, Margetis K. Large language models in patient education: a scoping review of applications in medicine. Frontiers in Medicine 2024;11 View
  3. Leon M, Ruaengsri C, Pelletier G, Bethencourt D, Shibata M, Flores M, Shudo Y. Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions. Journal of Clinical Medicine 2024;13(21):6543 View
  4. Abdelgadir Y, Thongprayoon C, Craici I, Cheungpasitporn W, Miao J. Enhancing Patient Comprehension of Glomerular Disease Treatments Using ChatGPT. Healthcare 2024;13(1):57 View
  5. Xiang K, Shi D. Personalized insights into liver disease management: a text mining analysis of online consultation data. Frontiers in Public Health 2025;13 View
  6. He F, Yang M, Liu J, Gong T, Ma J, Yang T, Zhao D, Li S, Tian D. Quality and reliability of pediatric pneumonia related short videos on mainstream platforms: cross-sectional study. BMC Public Health 2025;25(1) View