ArticleDrug safety2025
Off-the-Shelf Large Language Models for Causality Assessment of Individual Case Safety Reports: A Proof-of-Concept with COVID-19 Vaccines.
Article in Drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design.Pharmaceutical research · 2026Article
- A Scoping Review of Case-Level Causality Assessment Tools Developed Between 2008 and 2023; Strengths, Weaknesses and Potential Future Improvements.Drug safety · 2026Article
- Large Language Models in Adverse Drug Reaction Detection and Pharmacovigilance: A Systematic Review of Current Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Biomedical Large Language Models and Prompt Engineering for Causality Assessment of Individual Case Safety Reports in Pharmacovigilance.Pharmaceutical research · 2026Article
- 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.Journal of medical Internet research · 2026Article
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9 authors.
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Abstract
backgroundThis study evaluated the feasibility of ChatGPT and Gemini, two off-the-shelf large language models (LLMs), to automate causality assessments, focusing on Adverse Events Following Immunizations (AEFIs) of myocarditis and pericarditis related to COVID-19 vaccines.
methodsWe assessed 150 COVID-19-related cases of myocarditis and pericarditis reported to the Vaccine Adverse Event Reporting System (VAERS) in the United States of America (USA). Both LLMs and human experts conducted the World Health Organization (WHO) algorithm for vaccine causality assessments, and inter-rater agreement was measured using percentage agreement. Adherence to the WHO algorithm was evaluated by comparing LLM responses to the expected sequence of the algorithm. Statistical analyses, including descriptive statistics and Random Forest modeling, explored case complexity (e.g., string length measurements) and factors affecting LLM performance and adherence.
resultsChatGPT showed higher adherence to the WHO algorithm (34%) compared to Gemini (7%) and had moderate agreement (71%) with human experts, whereas Gemini had fair agreement (53%). Both LLMs often failed to recognize listed AEFIs, with ChatGPT and Gemini incorrectly identifying 6.7% and 13.3% of AEFIs, respectively. ChatGPT showed inconsistencies in 8.0% of cases and Gemini in 46.7%. For ChatGPT, adherence to the algorithm was associated with lower string complexity in prompt sections. The random forest analysis achieved an accuracy of 55% (95% confidence interval: 35.7-73.5) for predicting adherence to the WHO algorithm for ChatGPT.
conclusionNotable limitations of ChatGPT and Gemini have been identified in their use for aiding causality assessments in vaccine safety. ChatGPT performed better, with higher adherence and agreement with human experts. In the investigated scenario, both models are better suited as complementary tools to human expertise.
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