Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.

Journal: JMIR formative research
PMID: 39794312

Abstract

BACKGROUND: The COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an "infodemic" of misinformation, particularly prevalent in women's health, has emerged. This challenge has been pivotal for health care providers, especially gynecologists and obstetricians, in managing pregnant women's health. The pandemic heightened risks for pregnant women from COVID-19, necessitating balanced advice from specialists on vaccine safety versus known risks. In addition, the advent of generative artificial intelligence (AI), such as large language models (LLMs), offers promising support in health care. However, they necessitate rigorous testing.

Authors

  • Nicola Luigi Bragazzi
    Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Michèle Buchinger
    Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel.
  • Hisham Atwan
    Kaplan Medical Centre, Department of Internal Medicine, Hebrew University, Rehovot, Israel.
  • Ruba Tuma
    Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel.
  • Francesco Chirico
    Post-Graduate School of Occupational Health, Università Cattolica del Sacro Cuore, Rome, Italy.
  • Lukasz Szarpak
    Henry JN Taub Department of Emergency Medicine, Baylor College of Medicine, Houston, TX, United States.
  • Raymond Farah
    Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel.
  • Rola Khamisy-Farah
    Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel.

Keywords

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