ArticleCanadian Urological Association journal = Journal de l'Association des urologues du Canada2025
Use of artificial intelligence-generated multiple-choice questions for the examination of surgical subspecialty residents Report of feasibility and psychometric analysis.
Article in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in urological education: a systematic review.BMC medical education · 2026Pooled it
- Evaluating AI-generated examination papers in periodontology: a comparative study with human-designed counterparts.BMC medical education · 2025Trial
- AI-Generated Microlearning for Plastic Surgery Residency: Single-Arm Pre-Post Feasibility Study.JMIR medical education · 2026Article
- CUAJ at 20.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Article
- Evaluation of three artificial intelligence chatbots for generating clinical hematology multiple choice questions for medical students.Scientific reports · 2026Article
- Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study.JMIR medical education · 2025Article
- Resident assessment in urology: ChatGPT, take the wheel?Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2025Article
- Evaluating artificial intelligence-generated multiple-choice questions in clinical pathology.Academic pathologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMultiple-choice questions (MCQs) are essential in medical education and widely used by licensing bodies. They are traditionally created with intensive human effort to ensure validity. Recent advances in artificial intelligence (AI), particularly large language models (LLMs), offer the potential to streamline this process. This study aimed to develop and test a GPT-4 model with customized instructions for generating MCQs to assess urology residents.
methodsA GPT-4 model was embedded using guidelines from medical licensing bodies and reference materials specific to urology. This model was tasked with generating MCQs designed to mimic the format and content of the 2023 urology examination outlined by the Royal College of Physicians and Surgeons of Canada (RCPSC). Following generation, a selection of MCQs underwent expert review for validity and suitability.
resultsFrom an initial set of 123 generated MCQs, 60 were chosen for inclusion in an exam administered to 15 urology residents at the University of Toronto. The exam results demonstrated a general increasing performance with level of training cohorts, suggesting the MCQs' ability to effectively discriminate knowledge levels among residents. The majority (33/60) of the questions had discriminatory value that appeared acceptable (discriminatory index 0.2-0.4) or excellent (discriminatory index >0.4).
conclusionsThis study highlights AI-driven models like GPT-4 as efficient tools to aid with MCQ generation in medical education assessments. By automating MCQ creation while maintaining quality standards, AI can expedite processes. Future research should focus on refining AI applications in education to optimize assessments and enhance medical training and certification outcomes.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.