ArticleExploratory research in clinical and social pharmacy2026
Comparative performance of large language models and Drugs.com versus Lexicomp for antiseizure medication drug-drug interactions: A cross-sectional study with iterative prompting analysis.
Article in Exploratory research in clinical and social pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- AI-generated patient information leaflets for oral anticoagulants: quality, usability, readability, and reproducibility compared with FDA-referenced materials.International journal of clinical pharmacy · 2026Article
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6 authors.
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Abstract
Background: Antiseizure medications (ASMs) are frequently co-prescribed and are associated with a high risk of clinically significant drug-drug interactions (DDIs). Large language models (LLMs) are increasingly used for clinical queries, yet their performance in detecting ASM-related DDIs compared with established drug interaction databases remains uncertain. Methods: A cross-sectional comparative study evaluated 186 ASM-comedication pairs (126 classified as major/moderate by Lexicomp) using ChatGPT-4, DeepSeek-V3, and Drugs.com, with Lexicomp as the reference standard. Interactions were assessed for presence, severity, mechanism, and management recommendations. Major and moderate interactions were classified as clinically relevant for performance analysis. A post hoc iterative prompting analysis was conducted exclusively on false positive cases to assess improvements in specificity. Performance metrics included sensitivity, specificity, predictive values, and overall accuracy. Two clinical pharmacists independently assessed outputs for accuracy, clarity, and completeness, with adjudication by a third pharmacist. Results: Drugs.com demonstrated the best overall performance, with sensitivity 0.870, specificity 0.629, and accuracy 0.709. ChatGPT showed high sensitivity (0.842) but low specificity (0.358), reflecting frequent over prediction of interactions. Low specificity reflected frequent overclassification of non-interacting or minor pairs as clinically relevant, potentially leading to alert fatigue, unnecessary monitoring or therapeutic modification. DeepSeek achieved the highest sensitivity (0.877) but the lowest specificity (0.200) and the greatest number of false positives. Iterative prompting substantially improved specificity for ChatGPT (0.77) and DeepSeek (0.42), correcting many false positive classifications. Conclusion: ChatGPT and DeepSeek provided broad interaction overviews but demonstrated limited specificity under single-prompt conditions. Structured, evidence-constrained prompting improved specificity in false positive cases within this dataset. Drugs.com showed the most balanced performance compared with zero-shot LLM outputs. At present, LLMs may serve as supplementary explanatory tools, whereas structured drug interaction databases remain more reliable for primary DDI screening.
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