ArticleInternational journal of clinical pharmacy2026
Performance evaluation of large language models in real-world perinatal medication consultations: a cross-sectional study.
Article in International journal of clinical pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionPerinatal medication consultation is a core clinical pharmacy service that involves a complex benefit-risk assessment for both maternal and fetal safety. Large language models (LLMs) have emerged as potential tools to improve access to medication information, yet their performance and safety in real-world, pharmacist-led perinatal consultation settings, particularly in non-English contexts, remain insufficiently evaluated.
aimTo evaluate and compare the performance of multiple advanced large language models in addressing real-world Chinese perinatal medication consultation queries and to assess their potential role as supervised adjunctive tools within clinical pharmacy services.
methodThis cross-sectional study evaluated seven LLMs using real-world clinical data from pharmacist-led medication consultations at the Pharmacy Clinic of the Beijing Obstetrics and Gynecology Hospital, Capital Medical University. A standardized test set of 64 perinatal medication consultation questions was developed from 15,280 electronic consultation records collected between April 2014 and April 2024. The evaluated models included international (GPT-5.1, Grok 3, Gemini 3.0) and domestic (DeepSeek, Wenxin Yiyan, Kimi K2, Tongyi Qianwen) models. Senior clinical pharmacologists independently assessed responses across four dimensions-relevance, accuracy, usefulness, and empathy-using a 10-point Likert scale. Results are reported primarily as median (IQR), with mean ± SD additionally provided as a secondary descriptor to facilitate comparison with prior literature.
resultsAmong the 448 model-generated responses, inter-rater consistency was excellent (ICC = 0.91, 95% CI 0.88-0.94). Significant differences in overall performance were observed among the models (Kruskal-Wallis H = 187.4, p < 0.001; ε
conclusionLLMs have demonstrated variable performance in response to perinatal medication consultation queries. While high-performing models show potential to support pharmacist-led perinatal medication consultations by improving access to information, their current performance supports use only as supervised, adjunctive decision-support tools rather than independent sources of medication counseling, with human oversight essential prior to broader integration.
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