Evaluating a large language model (ChatGPT-5) for detecting potential drug–drug interactions in intensive care: a cross-sectional comparative study with a clinical decision support system

Background/Objectives Polypharmacy in intensive care units (ICUs) substantially increases the risk of potential drug–drug interactions (pDDIs), which may lead to adverse drug events and worsen clinical outcomes. Conventional rule-based clinical decision support systems (CDSSs), although widely used, are limited by static logic and restricted contextual reasoning. Recently, large language models (LLMs) such as ChatGPT have emerged as potential adjunctive tools capable of generating clinically interpretable outputs; however, their diagnostic reliability in safety-critical settings remains unclear. Methods This cross-sectional study aimed to evaluate the diagnostic performance of ChatGPT-5 in detecting clinically significant pDDIs in ICU patients, using the UpToDate Drug Interaction Checker as the reference comparator. Data were collected from 101 adult ICU patients on a predefined index date. The model referred to as “ChatGPT-5” corresponds to the version available via the OpenAI API in September 2025. A standardized prompt was applied for each patient to ensure consistency. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overall accuracy, and Cohen’s κ coefficient, were calculated. Results ChatGPT-5 identified a higher number of pDDIs per patient compared with UpToDate (median [IQR]: 6 vs. 0 [0–2]). The mean number of pDDIs per patient was also higher for ChatGPT-5 (7.2 ± 4.1) compared with UpToDate (1.1 ± 1.8). The diagnostic performance analysis was conducted at the patient level. Of the 101 included patients, 17 without a category D or X interaction in the UpToDate reference assessment were excluded from this analysis, leaving 84 patients. Sensitivity, specificity, and overall accuracy were 69.8%, 52.4%, and 65.5%, respectively, with a positive predictive value of 81.5% and negative predictive value of 36.7%. In a conservative full-cohort scenario analysis, treating the 17 excluded patients as discordant cases reduced overall exact agreement from 65.5% (55/84) to 54.5% (55/101). Agreement between the two systems was poor (κ = 0.19), indicating considerable variability in classification. Conclusion Although ChatGPT-5 demonstrated limited diagnostic performance and the ability to generate clinically interpretable explanations, its low specificity and limited agreement with a rule-based system highlight important safety concerns. These findings suggest that LLMs may serve as complementary tools rather than standalone solutions in ICU pharmacovigilance. Further multicenter validation studies are required before clinical integration.