Purpose: To assess the ChatGPT-4 (ChatGPT) large language model (LLM) on tasks relevant to community pharmacy. Methods: ChatGPT was assessed with community pharmacy-relevant test cases involving drug information retrieval, identifying labeling errors, prescription interpretation, decision making under uncertainty and multi-disciplinary consults. Drug information on rituximab, warfarin, and St. John’s wort was queried. The decision-support scenarios consisted of a subject with swollen eyelids, and a maculopapular rash in a subject on lisinopril and ferrous sulfate. The multi-disciplinary scenarios required integration of medication management with nutrition and physical therapy. Results: The responses from ChatGPT-4 for rituximab, warfarin, and St. John’s wort were satisfactory and cited drug databases and drug-specific monographs. ChatGPT identified labeling errors related to incorrect medication strength, form, administration route, unit conversion, and directions. For the patient with inflamed eyelids, the course of action developed by GPT-4 was comparable to the pharmacist’s approach. For the patient with the maculopapular rash, both the pharmacist and ChatGPT placed a drug reaction to either lisinopril or ferrous sulfate at the top of the differential. ChatGPT provided customized vaccination requirements for travel to Brazil, guidance on management of drug allergies, and recovery from a knee injury. ChatGPT provided satisfactory medication management and wellness information for a diabetic on metformin and semaglutide. Conclusions: LLMs have the potential to become a powerful tool in community pharmacy. However, testing in validation studies across diverse pharmacist queries, drug classes, and populations, and engineering to secure patient privacy will be needed to enhance LLM utility.

Background: Clinical trial simulations and pharmacometric modeling of biomarker profiles for under-represented groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. Objectives: To investigate generative adversarial networks (GANs), an artificial intelligence (AI) technology that enables realistic simulations of complex patterns, for modeling clinical biomarker profiles of under-represented groups. Methods: GANs consist of generator and discriminator neural networks that operate in tandem. GAN architectures were developed for modeling univariate and joint distributions of a panel of 16 diabetes-relevant biomarkers from the National Health and Nutrition Examination Survey (NHANES), which contains laboratory and clinical biomarker data from a population-based sample of individuals of all ages, racial groups, and ethnicities. Conditional GANs were used to model biomarker profiles for race/ethnicity categories. GAN performance was assessed by comparing GAN outputs to test data. Results: The biomarkers exhibited non-normal distributions and varied in their bivariate correlation patterns. Univariate distributions were modeled with generator and discriminator neural networks consisting of two dense layers with rectified linear unit-activation. The distributions of GAN-generated biomarkers were similar to the test data distributions. The joint distributions of the biomarker panel in the GAN-generated data were dispersed and overlapped with the joint distribution of the test data as assessed by three multi-dimensional projection methods. Conditional GANs satisfactorily modeled the joint distribution of the biomarker panel in the Black, Hispanic, White, and “Other” race/ethnicity categories. Conclusions: GAN are a promising AI approach for generating virtual patient data with realistic biomarker distributions for under-represented race/ethnicity groups.