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

Applications of AI (1006) Clinical Information and Decision Making (3794) Research Instruments, Questionnaires, and Tools (1214) Digital Health, Telehealth and e-Innovation in Clinical Settings (737) Natural Language Processing (1308) Digital Health Reporting Standards, Quality and Transparency in e-Research (236) Machine Learning (3254)

Published on in Vol 5 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/93761, first published .
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Retrieve-Then-Verify for Evaluating Evidence Support and Hallucination in Large Language Model–Generated Medical Information: Empirical Study

Retrieve-Then-Verify for Evaluating Evidence Support and Hallucination in Large Language Model–Generated Medical Information: Empirical Study

Authors of this article:

Zhaohui Liang1 Author Orcid Image ;   Cynthia Sheffield2 Author Orcid Image ;   Gisela Butera2 Author Orcid Image ;   Sameer Antani1 Author Orcid Image
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Zhaohui Liang   1 , PhD ;   Cynthia Sheffield   2 , MBA ;   Gisela Butera   2 , MEd ;   Sameer Antani   1 , PhD

1 Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States

2 NIH Library, National Institutes of Health, Bethesda, MD, United States

Corresponding Author:

  • Sameer Antani, PhD
  • Division of Intramural Research
  • National Library of Medicine
  • National Institutes of Health
  • 8600 Rockville Pike
  • Bethesda, MD 20894-3824
  • United States
  • Phone: 1 301-435-3218
  • Email: sameer.antani@nih.gov