This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3359) Artificial Intelligence (2252) Epublishing and Open Access (107) Generative Language Models Including ChatGPT (760) New Resources for Medical Education (125) New Methods (1154) New Methods and Approaches in Medical Education (400) Digital Health Meta-Research and Bibliographic Studies (68) Artificial Intelligence (AI) in Medical Education (209)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69707, first published .
Using Large Language Models to Summarize Evidence in Biomedical Articles: Exploratory Comparison Between AI- and Human-Annotated Bibliographies

Using Large Language Models to Summarize Evidence in Biomedical Articles: Exploratory Comparison Between AI- and Human-Annotated Bibliographies

Using Large Language Models to Summarize Evidence in Biomedical Articles: Exploratory Comparison Between AI- and Human-Annotated Bibliographies

Authors of this article:

Michelle Colder Carras1 Author Orcid Image ;   Riaz Qureshi2 Author Orcid Image ;   Kevin Naaman2 Author Orcid Image ;   Faisal Aldayel3 Author Orcid Image ;   Mayank Date3 Author Orcid Image ;   Dahlia AlJuboori3 Author Orcid Image ;   Johannes Thrul3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Michelle Colder Carras   1 , PhD ;   Riaz Qureshi   2 , PhD ;   Kevin Naaman   2 , PhD ;   Faisal Aldayel   3 , MD, MPH ;   Mayank Date   3 , BDS, MPH ;   Dahlia AlJuboori   3 , MBChB, MHS ;   Johannes Thrul   3 , PhD

1 Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States

2 Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

3 Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States

Corresponding Author:

  • Michelle Colder Carras, PhD
  • Department of International Health
  • Johns Hopkins Bloomberg School of Public Health
  • 615 N Wolfe St
  • Baltimore, MD 21205
  • United States
  • Phone: 1 410-955-3934
  • Email: mcarras@jhu.edu