ArticleCureus2026
ChatGPT as a Tool for Perioperative Safety: Evaluating Drug Interaction Detection in Anesthetic Regimens.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Drug-drug interactions (DDIs) are a major contributor to perioperative complications, yet no prior studies have evaluated the performance of large language models in this high-risk setting. This study assessed the accuracy of ChatGPT, GPT-5, free version (OpenAI, San Francisco, USA) in identifying DDIs in anesthetic regimens compared with the gold-standard reference Lexicomp. Methods We conducted an analytical cross-sectional study using 40 synthetic perioperative vignettes generated by OpenEvidence and reviewed by an anesthesiologist. Each vignette was reviewed for clinically significant DDIs using Lexicomp's "Interactions" feature. ChatGPT was queried twice with standardized prompts, and responses were classified as Correct or Incorrect. Results Across both trials, ChatGPT accurately identified 76 of 80 clinically significant DDIs (sensitivity 95%) with four responses classified as Incorrect. These responses were not entirely erroneous, as each recognized at least one major interaction, yet they omitted additional clinically relevant interactions or failed to provide comprehensive management recommendations. Notably, ChatGPT frequently supplemented its responses with relevant contextual information, including age-specific considerations and perioperative monitoring strategies. Conclusion ChatGPT demonstrated high accuracy in detecting clinically significant DDIs in perioperative anesthetic regimens. Its contextual insights may enhance clinical decision-making; however, occasional omissions and limitations in sourcing warrant cautious integration into practice. Further studies in real-world perioperative settings are needed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.