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

Digital Health Reviews (1774) Applications of AI (311) Responsible Health AI (64) AI Language Models in Health Care (222) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (220) Machine Learning (2159) Natural Language Processing (934) Secondary Use of Clinical Data for Research and Surveillance (482) Artificial Intelligence (2234) Ethics, Privacy, and Legal Issues (634)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68291, first published .
Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review

Authors of this article:

David Guo1 Author Orcid Image ;   Kim-Kwang Raymond Choo1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Nwokedi V, Odedele M, Yusuff T, Anderson I, Akanbi O, Agu C, Omoike A. Developing and Validating Predictive Models for Adverse Drug Reactions using Electronic Health Records (EHRs): A Narrative Review. Epidemiology and Health Data Insights 2025;1(6):ehdi019 View
  2. Chinnadurai R, Madabhushi A. Opportunities with artificial intelligence in assessing the potency of mesenchymal stromal cells. Stem Cells 2026;44(1) View
  3. Jing H, Jie D, Zhong W, Xiao M, Qingxuan Z, Sha L, Shuai Z, Yunyan X, Mingxiao L, Yahui L. Active monitoring vs. spontaneous reporting of antineoplastic drug–related adverse drug reactions: evidence from the Chinese hospital pharmacovigilance system. Frontiers in Health Services 2026;5 View