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Updated: Sep 9, 2025

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From theory to practice: Evaluating AI in pharmacy.

Steven M Smoke1, Alice Hou2, Scott D Nelson2

  • 1RWJBarnabas Health, West Orange, NJ, USA.

American Journal of Health-System Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists
|September 1, 2025
PubMed
Summary

Pharmacists must understand artificial intelligence (AI) model evaluation to improve patient care and pharmacy operations. This guide helps pharmacists critically assess AI studies and metrics for effective integration.

Keywords:
algorithmovigilanceartificial intelligencegenerative modelsmachine learningmodel evaluationpatient carepharmacy operationspharmacy practicepredictive models

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Area of Science:

  • Pharmacy Practice
  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) is increasingly important in healthcare, particularly in pharmacy.
  • Pharmacists need skills to evaluate AI models and studies for effective implementation.
  • Understanding AI evaluation is key to leveraging its potential in pharmacy.

Purpose of the Study:

  • To provide pharmacists with a guide for evaluating AI models and studies.
  • To emphasize critical assessment of AI definitions, data, populations, and training processes.
  • To discuss common performance metrics for AI in healthcare.

Main Methods:

  • Review of AI model evaluation principles relevant to pharmacy practice.
  • Discussion of key components in assessing AI studies, including data quality and validation.
  • Explanation of standard performance metrics used in AI research.

Main Results:

  • Pharmacists can effectively evaluate AI models by understanding core principles.
  • Critical assessment of AI studies ensures responsible and effective technology integration.
  • Knowledge of AI evaluation metrics aids in informed decision-making.

Conclusions:

  • AI has significant potential to transform pharmacy practice and patient care.
  • Equipping pharmacists with AI evaluation skills is crucial for responsible implementation.
  • A holistic approach to AI integration enhances patient care and operational efficiency.