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AI-generated questions for urological competency assessment: a prospective educational study.

Mert Başaranoğlu1, Erdem Akbay2, Erim Erdem2

  • 1;Department of Urology, Mersin University Faculty of Medicine, Mersin, Turkey. mertbasaranoglu@gmail.com.

BMC Medical Education
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) generated questions show promise for assessing urological competency in medical interns. While effective for formative assessment, expert oversight is crucial for high-stakes evaluations.

Keywords:
Artificial intelligenceAssessment systemsMedical educationUrology education

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Urology Assessment

Background:

  • Traditional medical assessment question development is time-consuming and struggles to keep pace with evolving knowledge.
  • AI integration in specialty-specific medical education assessment, particularly during clinical rotations, is largely unexplored.
  • This study addresses the need for efficient and effective assessment tools in urology.

Purpose of the Study:

  • To evaluate the effectiveness of AI-generated multiple-choice questions in assessing urological competency among medical interns.
  • To compare the technical accuracy and clinical relevance of questions generated by different AI models (ChatGPT and Gemini).
  • To determine the suitability of AI-generated questions for both formative and summative assessments in urology.

Main Methods:

  • Two AI language models generated 300 multiple-choice questions across six urological subspecialties.
  • Seven experienced urologists validated questions based on technical accuracy, clinical relevance, and construct validity using a modified Delphi approach.
  • 100 validated questions were selected based on expert consensus.

Main Results:

  • Medical intern performance significantly improved throughout the rotation, indicating effective knowledge acquisition.
  • AI platforms demonstrated comparable technical accuracy (ChatGPT: 84.3%, Gemini: 83.8%).
  • Clinical scenario questions showed superior discrimination compared to recall questions (0.28 vs. 0.14, p < 0.001).

Conclusions:

  • AI-generated questions possess appropriate technical accuracy and difficulty for assessing urological clinical competency.
  • AI tools show potential for formative assessment, especially with clinical scenarios, but require careful consideration for high-stakes testing due to current discrimination limitations.
  • Integration of AI-generated questions in specialty-specific assessments is feasible with expert oversight and continuous validation.