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Related Experiment Video

Updated: Jul 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Mapping artificial intelligence integration in objective structured clinical examinations: A scoping review.

Sergio Andrés León-Ariza1, María Camila Orobio-Pinzón1, Héctor Miguel Ibáñez-Gutiérrez1

  • 1School of Medicine, Universidad de los Andes, Bogotá, Colombia.

Medical Teacher
|July 11, 2026
PubMed
Summary

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Artificial intelligence (AI) in Objective Structured Clinical Examinations (OSCEs) primarily enhances structured tasks like grading, but struggles with relational competencies. Realizing AI

Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Clinical Competency Assessment

Background:

  • Objective Structured Clinical Examinations (OSCEs) are crucial for assessing clinical competence but face challenges like examiner workload and scoring variability.
  • Artificial Intelligence (AI) presents potential solutions to enhance OSCEs and support precision medical education, yet evidence is fragmented.
  • This scoping review aims to map the current landscape of AI applications within OSCEs.

Purpose of the Study:

  • To conduct a comprehensive scoping review of Artificial Intelligence (AI) applications in Objective Structured Clinical Examinations (OSCEs).
  • To identify the forms of AI used, their integration across OSCE phases, and the competencies they target.
  • To analyze the implications of AI in OSCEs for faculty, resources, and ethical considerations.
Keywords:
Artificial intelligencehealth professions educationobjective structured clinical examinationprecision medical educationprogrammatic assessment

Related Experiment Videos

Last Updated: Jul 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Main Methods:

  • Systematic literature search following PRISMA-ScR guidelines across multiple databases (MEDLINE, Scopus, Web of Science, etc.) up to June 2025.
  • Inclusion of studies examining AI in any phase of health professions OSCEs.
  • Data extraction focused on AI type, technology, OSCE phase, competencies assessed, outcomes, resource implications, and ethical issues, using deductive and inductive coding.

Main Results:

  • Twenty-two studies were included, revealing AI applications in learner preparation, station development, scoring, and operational delivery.
  • AI demonstrated benefits in grading, feedback speed, and consistency for structured tasks, but showed limitations with relational competencies.
  • Most AI applications augmented or modified existing OSCE processes (SAMR model), with personalization being a key P4 characteristic; ethical concerns included privacy, bias, and transparency.

Conclusions:

  • AI currently augments OSCEs, excelling in structured tasks but falling short in assessing relational and situated competencies.
  • Evidence supporting AI's role in delivering precision medical education through OSCEs remains insufficient; P4 alignment is partial.
  • Effective AI integration in OSCEs requires human-in-the-loop governance, robust safeguards, and equitable implementation, especially in resource-limited settings.