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Evaluating large language models for pharmacotherapy simulations: a mixed-methods study.

Ahmed N Farrag1, Amany El-Zeiny2, Amani M Ali3

  • 1Department of Clinical Pharmacy, Faculty of Pharmacy, Cairo University, Cairo, Egypt. ahmed.farrag@cu.edu.eg.

NPJ Digital Medicine
|May 5, 2026
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Summary
This summary is machine-generated.

Large language models (LLMs) show potential for clinical pharmacy simulations, but accuracy and safety require expert validation. While students preferred LLM-generated cases, expert review found significant clinical errors, especially in acute myeloid leukemia simulations.

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Area of Science:

  • Pharmacy Education
  • Artificial Intelligence in Medicine
  • Clinical Simulation

Background:

  • Simulation-based learning is crucial for clinical pharmacy education but faces scalability challenges due to faculty resource limitations.
  • Large language models (LLMs) present a potential solution for scalable simulation generation, but their pedagogical effectiveness and clinical reliability need thorough evaluation.

Purpose of the Study:

  • To evaluate the pedagogical rigor and clinical reliability of LLM-generated clinical pharmacy simulations.
  • To compare student satisfaction with LLM-based simulations versus traditional methods.
  • To identify specific areas of weakness in LLM-generated clinical case simulations.

Main Methods:

  • A mixed-methods, counterbalanced study involving 104 PharmD students interacting with LLM-generated acute myeloid leukemia (AML) or chronic myeloid leukemia (CML) cases.
  • Expert panel evaluation of simulation sessions based on clinical authenticity, instructional design, and clinical reasoning.
  • Student satisfaction surveys comparing LLM-based simulations to traditional methods.

Main Results:

  • Over half (51.5%) of the 103 evaluated LLM-generated sessions met passing criteria across all domains.
  • Clinical accuracy and safety were the most significant limiting factors (58.3%), while clinical reasoning (81.6%) and instructional design (82.5%) scored higher.
  • Chronic myeloid leukemia (CML) simulations performed significantly better (62.3%) than acute myeloid leukemia (AML) simulations (40.0%).
  • Identified errors included guideline misalignment, pharmacotherapeutic inaccuracies, fabricated evidence, and cross-condition recommendations, predominantly in AML cases.
  • Students reported higher satisfaction with LLM-based simulations (49.8%) compared to traditional methods (30.0%), but this did not correlate with expert-assessed quality.

Conclusions:

  • LLM-generated simulations show promise for clinical pharmacy education but require robust expert oversight and validation for clinical accuracy and safety.
  • Platform-specific and disease-specific validation are essential before widespread educational deployment.
  • Further effectiveness trials are needed to assess objective learning outcomes.