Evidence map›Paper›PMID 38647684›Full record

ArticleEuropean 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 Surgery2024

ChatGPT's adherence to otolaryngology clinical practice guidelines.

Idit Tessler, Amit Wolfovitz, Eran E Alon, Nir A Gecel, Nir Livneh, Eyal Zimlichman, Eyal Klang

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Article in 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, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

  1. 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
  2. Artificial Intelligence Models for Dysphonia Patient Education.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. ChatGPT versus DeepSeek in head and neck cancer staging and treatment planning: guideline-based study.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
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  8. Review
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  10. Accuracy of ChatGPT responses on tracheotomy for patient education.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 · 2024
    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

7 authors.

Idit Tessler *Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan, Israel. idit.tessler@gmail.com.ORCID http://orcid.org/0000-0002-3297-7022
Amit Wolfovitz *Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan, Israel.
Eran E AlonDepartment of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan, Israel.
Nir A GecelSchool of Medicine, Tel Aviv University, Tel Aviv, Israel.
Nir LivnehDepartment of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan, Israel.
Eyal ZimlichmanSchool of Medicine, Tel Aviv University, Tel Aviv, Israel.
Eyal KlangThe Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesLarge language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines.

methodsWe presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses.

resultsThe study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests.

conclusionsUsing a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use.

Indexed as

Guideline AdherenceOtolaryngologyPractice Guidelines as TopicHumansUnited StatesArtificial intelligenceChatGPTClinical practice guidelinesMedical knowledgeOtolaryngology

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Registered trials

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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.