Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

ABSTRACT: Drug-drug 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-based large language model) to predict DDIs in a real-world 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-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low

SUBMITTER: Radha Krishnan RP 

PROVIDER: S-EPMC11602951 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature