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ChatGPT Generated Otorhinolaryngology Multiple-Choice Questions: Quality, Psychometric Properties, and Suitability for Assessments.
Article in OTO open. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study.JMIR medical education · 2026Article
- Quality of Large Language Model-Generated MCQs Across Three Medical Disciplines: An Expert Rater-Based Comparison of Gemini, GPT-4 and Perplexity Pro.Advances in medical education and practice · 2026Article
- Benchmarking ChatGPT-generated multiple-choice questions against faculty-authored items in dental education.Scientific reports · 2025Article
- Diagnostic Performance of ChatGPT-4o in Analyzing Oral Mucosal Lesions: A Comparative Study with Experts.Medicina (Kaunas, Lithuania) · 2025Article
- Using ChatGPT as an assessment tool for medical residents in Mexico: a descriptive experience.Frontiers in artificial intelligence · 2025Article
- Comparing ChatGPT-3.5 and ChatGPT-4's alignments with the German evidence-based S3 guideline for adult soft tissue sarcoma.iScience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To explore Chat Generative Pretrained Transformer's (ChatGPT's) capability to create multiple-choice questions about otorhinolaryngology (ORL). Study Design: Experimental question generation and exam simulation. Setting: Tertiary academic center. Methods: ChatGPT 3.5 was prompted: "Can you please create a challenging 20-question multiple-choice questionnaire about clinical cases in otolaryngology, offering five answer options?." The generated questionnaire was sent to medical students, residents, and consultants. Questions were investigated regarding quality criteria. Answers were anonymized and the resulting data was analyzed in terms of difficulty and internal consistency. Results: ChatGPT 3.5 generated 20 exam questions of which 1 question was considered off-topic, 3 questions had a false answer, and 3 questions had multiple correct answers. Subspecialty theme repartition was as follows: 5 questions were on otology, 5 about rhinology, and 10 questions addressed head and neck. The qualities of focus and relevance were good while the vignette and distractor qualities were low. The level of difficulty was suitable for undergraduate medical students (n = 24), but too easy for residents (n = 30) or consultants (n = 10) in ORL. Cronbach's Conclusion: ChatGPT 3.5 is able to generate grammatically correct simple ORL multiple choice questions for a medical student level. However, the overall quality of the questions was average, needing thorough review and revision by a medical expert to ensure suitability in future exams.
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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.