ArticleOTO open
Assessment of Artificial Intelligence Performance on the Otolaryngology Residency In-Service Exam.
Article in OTO open. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Evaluating the Potential and Accuracy of ChatGPT-3.5 and 4.0 in Medical Licensing and In-Training Examinations: Systematic Review and Meta-Analysis.JMIR medical education · 2025Pooled it
- Generative AI for Personalized Learning Platforms in Otolaryngology-Head and Neck Surgery Education: Scoping Review.JMIR medical education · 2026Review
- ChatGPT Versus DeepSeek in Resectability Categorization of Chronic Rhinosinusitis: A Comparison Based on EPOS 2020.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Evaluation of AI models for radiology exam preparation: DeepSeek vs. ChatGPT-3.5.Medical education online · 2025Article
- Determining the reliability of Artificial Intelligence programs in medical faculty board exams.Pakistan journal of medical sciences · 2025Article
- Applications and Future Perspectives of Large Language Models in Otolaryngology-Head and Neck Surgery: A Comprehensive Survey.Clinical and experimental otorhinolaryngology · 2025Review
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Exploring the potential of artificial intelligence chatbots in prosthodontics education.BMC medical education · 2025Article
- Performance of Novel GPT-4 in Otolaryngology Knowledge Assessment.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 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
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study seeks to determine the potential use and reliability of a large language learning model for answering questions in a sub-specialized area of medicine, specifically practice exam questions in otolaryngology-head and neck surgery and assess its current efficacy for surgical trainees and learners. Study Design and Setting: All available questions from a public, paid-access question bank were manually input through ChatGPT. Methods: Outputs from ChatGPT were compared against the benchmark of the answers and explanations from the question bank. Questions were assessed in 2 domains: accuracy and comprehensiveness of explanations. Results: Overall, our study demonstrates a ChatGPT correct answer rate of 53% and a correct explanation rate of 54%. We find that with increasing difficulty of questions there is a decreasing rate of answer and explanation accuracy. Conclusion: Currently, artificial intelligence-driven learning platforms are not robust enough to be reliable medical education resources to assist learners in sub-specialty specific patient decision making scenarios.
Indexed as
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.