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Comparative Analysis of Generative Artificial Intelligence Systems in Solving Clinical Pharmacy Problems: Mixed

Lulu Li1, Pengqiang Du1, Xiaojing Huang1

  • 1Department of Pharmacy, Fuwai Central China Cardiovascular Hospital, 1 Fuwai Road, Zhengdong New District, Zhengzhou, China, 86 18538298379.

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|July 24, 2025
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Summary
This summary is machine-generated.

Generative AI shows promise in clinical pharmacy but has critical limitations like high-risk errors and reasoning gaps. DeepSeek-R1 performed best, yet human oversight is crucial for safe AI deployment.

Keywords:
DeepSeek-R1artificial intelligenceclinical pharmacycomparative analysisgenerative AI

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Area of Science:

  • Clinical Pharmacy
  • Artificial Intelligence
  • Health Informatics

Background:

  • Generative artificial intelligence (AI) systems are increasingly used in clinical pharmacy practice.
  • Systematic evaluations of AI efficacy, limitations, and risks in diverse pharmacy settings are limited.

Purpose of the Study:

  • To quantitatively evaluate and compare the performance of 8 mainstream generative AI systems.
  • To assess AI performance across 4 core clinical pharmacy scenarios: medication consultation, education, prescription review, and case analysis.
  • To utilize a multidimensional framework for comprehensive AI performance assessment.

Main Methods:

  • Selected 48 clinically validated questions from real-world pharmacy practice sources.
  • Tested 8 generative AI systems (ERNIE Bot, Doubao, Kimi, Qwen, GPT-4o, Gemini-1.5-Pro, Claude-3.5-Sonnet, DeepSeek-R1) using standardized prompts.
  • Employed a double-blind scoring design with 6 experienced clinical pharmacists evaluating responses across 6 dimensions (accuracy, rigor, applicability, coherence, conciseness, universality).

Main Results:

  • DeepSeek-R1 demonstrated the highest overall performance, particularly in complex tasks (P<.05).
  • Significant limitations observed across models include high-risk errors (e.g., 75% omitted contraindications) and lack of localization (e.g., 90% recommended inappropriate antibiotics for resistant strains).
  • Complex reasoning deficits were noted, with only one model detecting a gender-diagnosis contradiction and none identifying a specific prescription limit.

Conclusions:

  • Generative AI holds promise as a pharmacist assistance tool but cannot yet support autonomous clinical decision-making due to significant errors and reasoning gaps.
  • DeepSeek-R1 currently leads in performance, but all systems require optimization for knowledge updating, complex reasoning, and output interpretability.
  • Future deployment necessitates prioritizing human oversight, ethical safeguards, and continuous evaluation frameworks for safe integration.