{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:47:05Z","timestamp":1791820025007,"version":"4.3.4"},"reference-count":27,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:00:00Z","timestamp":1727827200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brit J Clinical Pharma"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:p>Drug\u2013drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence\u2010based large language model) to predict DDIs in a real\u2010world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter\u2010rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning \u2018drug interaction\u2019. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077\u20130.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real\u2010world scenarios.<\/jats:p>","DOI":"10.1111\/bcp.16275","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T05:55:03Z","timestamp":1727934903000},"page":"3361-3366","source":"Crossref","is-referenced-by-count":30,"title":["Evaluating the capability of ChatGPT in predicting drug\u2013drug interactions: Real\u2010world evidence using hospitalized patient data"],"prefix":"10.1111","volume":"90","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3495-5919","authenticated-orcid":false,"given":"Ramya Padmavathy","family":"Radha Krishnan","sequence":"first","affiliation":[{"name":"Faculty of Medicine and Health University of Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Euniss 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