ArticleCureus2023
Screening the Drug-Drug Interactions Between Antimicrobials and Other Prescribed Medications Using Google Bard and Lexicomp® Online™ Database.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Point-Prevalence of Antimicrobial-Related Potential Drug-Drug Interactions in Hospitalized Older Adults: A Multicenter Study Using Lexicomp.Journal of clinical medicine · 2026Article
- Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025Article
- Prevalence and clinical significance of potential drug-drug interactions of antimicrobials in Intensive Care Unit patients: a retrospective study.BMC pharmacology & toxicology · 2025Article
- A scoping review on generative AI and large language models in mitigating medication related harm.NPJ digital medicine · 2025Article
- Medication use and potential drug-drug interactions in a general surgery clinic: A descriptive study.Heliyon · 2025Article
- Evaluating AI performance in infectious disease education: a comparative analysis of ChatGPT, Google Bard, Perplexity AI, Microsoft Copilot, and Meta AI.Frontiers in medicine · 2025Article
- Large language models facilitating modern molecular biology and novel drug development.Frontiers in pharmacology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim This study aimed to critically appraise the drug-drug interaction (DDI) screening performance of Google Bard (Google AI, Mountain View, California, United States) by comparing it with the authorized Lexicomp® Online™ database (Wolters Kluwer Health, Philadelphia, Pennsylvania, United States). Methods This cross-sectional study was conducted between April 2023 and August 2023, and enrolled 414 prescriptions that had been collected randomly between April 2023 and June 2023. These prescriptions were processed individually by Lexicomp online and Google Bard to screen for DDIs between antimicrobials and other prescribed medications. Results The total number of DDIs based on Lexicomp and Google Bard were 90 and 68, respectively. Cohen's Kappa (κ) values showed that there was a nil to slight agreement between Lexicomp and Google Bard regarding the DDI risk rating (κ=0.01). Regarding the severity rate, there was a slight agreement between them (κ=0.02), but in terms of reliability rate, there was no agreement (κ =-0.02). Conclusion This study unveiled differences between Lexicomp and Google Bard regarding their DDI identification, severity rating, and reliability rates. It is fundamental to consider that both tools have their strengths and weaknesses and, therefore, should not be individually depended on for final clinical decisions. However, Lexicomp can be considered authoritative in screening DDIs, but Google Bard currently lacks the necessary precision and reliability for conducting such screenings.
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Registered trials
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