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Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/93237, first published .
Infographic comparing LLM performance in drug-adverse event causality assessment with expert evaluation.

Large Language Models for World Health Organization–Uppsala Monitoring Centre Drug–Adverse Event Causality Assessment Using Food and Drug Administration Adverse Event Reporting System Cases: Comparative Performance Study

Large Language Models for World Health Organization–Uppsala Monitoring Centre Drug–Adverse Event Causality Assessment Using Food and Drug Administration Adverse Event Reporting System Cases: Comparative Performance Study

Young Mi Ha   1 , MSc ;   Minjung Kim   1 , PharmD ;   YoungIn Bang   2 ;   Daejin Choi   3, 4 , PhD ;   Jae Hyun Kim   5 , PhD ;   Sandy Jeong Rhie   1, 2 , PharmD, PhD ;   Yoshihiro Noguchi   6 , PhD ;   Myeong Gyu Kim   1, 2, 4 , PhD

1 Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea

2 College of Pharmacy, Ewha Womans University, Seoul, Republic of Korea

3 College of Artificial Intelligence, Ewha Womans University, Seoul, Republic of Korea

4 Human-Centered Artificial Intelligence Research Institute, Ewha Womans University, Seoul, Republic of Korea

5 School of Pharmacy and Institute of New Drug Development, Jeonbuk National University, Jeonju, Republic of Korea

6 Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, Gifu, Japan

Corresponding Author:

  • Myeong Gyu Kim, PhD
  • Graduate School of Pharmaceutical Sciences
  • Ewha Womans University
  • 52 ewhayeodae-gil seodaemun-gu
  • Seoul 03760
  • Republic of Korea
  • Phone: 82 02-3277-3102
  • Email: kimmg@ewha.ac.kr