Evidence map›Paper›PMID 38227351›Full record

ArticleJMIR medical education2024

A Novel Evaluation Model for Assessing ChatGPT on Otolaryngology-Head and Neck Surgery Certification Examinations: Performance Study.

Cai Long, Kayle Lowe, Jessica Zhang, André Dos Santos, Alaa Alanazi, Daniel O'Brien, Erin D Wright, David Cote

Abstract read
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Turing problems in otolaryngology: a scoping review of the principal challenges of artificial ıntelligence and large language models.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Review
  4. Article
  5. Guideline-based benchmarking of large language models in otorhinolaryngology using 250 real-world cases.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Parental Perception on Usage of AI Chatbot to Understand Paediatric Otorhinolaryngology Condition: A Survey.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2025
    Article
  13. Chasing sleep physicians: ChatGPT-4o on the interpretation of polysomnographic results.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
    Article
  14. Observational
  15. Review
  16. Exploring the Ability of ChatGPT to Act as a Research Aid in Otolaryngology.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024
    Article
  17. Article
  18. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Cai LongDivision of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0002-5311-7355
Kayle LoweFaculty of Medicine, University of Alberta, Edmonton, AB, Canada.ORCID 0009-0006-7940-333X
Jessica ZhangFaculty of Medicine, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0002-1578-8529
André Dos SantosAlberta Machine Intelligence Institute, Edmonton, AB, Canada.ORCID 0009-0000-0393-3082
Alaa AlanaziDivision of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0001-8096-9118
Daniel O'BrienDepartment of Surgery, Creighton University, Omaha, NE, United States.ORCID 0000-0002-8394-9902
Erin D WrightDivision of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0001-5601-2754
David CoteDivision of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0001-8971-6969

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT is among the most popular large language models (LLMs), exhibiting proficiency in various standardized tests, including multiple-choice medical board examinations. However, its performance on otolaryngology-head and neck surgery (OHNS) certification examinations and open-ended medical board certification examinations has not been reported.

objectiveWe aimed to evaluate the performance of ChatGPT on OHNS board examinations and propose a novel method to assess an AI model's performance on open-ended medical board examination questions.

methodsTwenty-one open-ended questions were adopted from the Royal College of Physicians and Surgeons of Canada's sample examination to query ChatGPT on April 11, 2023, with and without prompts. A new model, named Concordance, Validity, Safety, Competency (CVSC), was developed to evaluate its performance.

resultsIn an open-ended question assessment, ChatGPT achieved a passing mark (an average of 75% across 3 trials) in the attempts and demonstrated higher accuracy with prompts. The model demonstrated high concordance (92.06%) and satisfactory validity. While demonstrating considerable consistency in regenerating answers, it often provided only partially correct responses. Notably, concerning features such as hallucinations and self-conflicting answers were observed.

conclusionsChatGPT achieved a passing score in the sample examination and demonstrated the potential to pass the OHNS certification examination of the Royal College of Physicians and Surgeons of Canada. Some concerns remain due to its hallucinations, which could pose risks to patient safety. Further adjustments are necessary to yield safer and more accurate answers for clinical implementation.

Indexed as

OtolaryngologySurgeonsCanadaCertificationHallucinationsHumansAIansweranswersartificial intelligencechatbotchatbotsChatGPTclinical implementationearENTexamexaminationexaminationsexamslanguage modellarge language modelslaryngologyLLMLLMsmachine learningmedical educationmedical examinationmedical licensingnatural language processingNLPnoseOHNSotolaryngologyotolaryngology/head and neck surgeryotologypatient safetyresponseresponsessafetysurgerysurgicalthroatwide range information

Identifiers

PMID38227351
PMCPMC10828939

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.