ArticleTherapeutic advances in drug safety2025
Can large language models detect drug-drug interactions leading to adverse drug reactions?
Article in Therapeutic advances in drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Applying machine learning to pharmacovigilance data: A proof-of-concept study.British journal of clinical pharmacology · 2026Article
- Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification.Journal of managed care & specialty pharmacy · 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
- Safety Monitoring of High-Risk Antibiotics Using Artificial Intelligence: A Narrative Review with Focus on Real-World Evidence.Life (Basel, Switzerland) · 2026Review
- 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
- Do large language models differ in their pharmacology-related response quality for oral and maxillofacial surgery? a blinded expert benchmark study.BMC oral health · 2026Article
- PromptSE: drug side effect prediction with LLM-derived pharmacological representations.Scientific reports · 2026Article
- How is AI developing in pharmacovigilance?Therapeutic advances in drug safety · 2026Article
- Confident but unsupported: auditing large language models against supplied evidence boundaries in drug-induced liver injury assessment.Frontiers in public health · 2026Article
- Performance evaluation of 9 large language models in anticoagulation decision-making for nonvalvular atrial fibrillation.Research and practice in thrombosis and haemostasis · 2026Article
- Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025Article
- Comparative evaluation of artificial intelligence platforms and drug interaction screening databases using real-world patient data.Exploratory research in clinical and social pharmacy · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
Background: Drug-drug interactions (DDI) are an important cause of adverse drug reactions (ADRs). Could large language models (LLMs) serve as valuable tools for pharmacovigilance specialists in detecting DDIs that lead to ADR notifications? Objective: To compare the performance of three LLMs (ChatGPT, Gemini, and Claude) in detecting and explaining clinically significant DDIs that have led to an ADR. Design: Observational cross-sectional study. Methods: We used the French National Pharmacovigilance Database to randomly extract Individual Case Safety Reports (ICSRs) of ADRs with DDI (positive controls) and ICSRs of ADRs without DDI (negative controls) registered in 2022. Interaction cases were classified by difficulty level (level-1 DDI being the easiest and level-2 DDI being the most difficult). We give each LLM (ChatGPT, Gemini, and Claude) the same prompt and case summary. Sensitivity, specificity, and Results: We assessed 82 ICSRs with DDIs and 22 ICSRs without DDIs. Among ICSRs with DDIs, 37 involved level-1 DDIs, and 45 involved level-2 DDIs. Correct responses were more frequent for level-1 DDIs than for level-2 DDIs. Regardless of difficulty level, ChatGPT detected 99% of DDI cases, and Claude and Gemini detected 95%. The percentage of correct answers to all DDI-related questions was 66% for ChatGPT, 68% for Claude, and 33% for Gemini. ChatGPT and Claude produced comparable results and outperformed Gemini ( Conclusion: LLMs can detect DDIs leading to pharmacovigilance cases, but cannot reliably exclude DDIs in cases without interactions. Pharmacologists are crucial for assessing whether a DDI is implicated in an ADR.
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