Evidence map›Paper›PMID 39776758›Full record

ArticleOTO open

Utilization of ChatGPT for Rhinology Patient Education: Limitations in a Surgical Sub-Specialty.

Alice E Huang, Michael T Chang, Ashoke Khanwalkar, Carol H Yan, Katie M Phillips, Michael J Yong, Jayakar V Nayak, Peter H Hwang, Zara M Patel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in OTO open. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Alice E HuangDepartment of Otolaryngology-Head and Neck Surgery Stanford University School of Medicine Stanford California USA.ORCID 0000-0002-8326-4997
Michael T ChangDepartment of Otolaryngology-Head and Neck Surgery Stanford University School of Medicine Stanford California USA.
Ashoke KhanwalkarDepartment of Otolaryngology-Head and Neck Surgery University of Colorado Anschultz School of Medicine Aurora Colorado USA.
Carol H YanDepartment of Otolaryngology-Head and Neck Surgery University of California-San Diego School of Medicine San Diego California USA.
Katie M PhillipsDepartment of Otolaryngology-Head and Neck Surgery University of Cincinnati College of Medicine Cincinatti Ohio USA.
Michael J YongDepartment of Otolaryngology-Head and Neck Surgery Stanford University School of Medicine Stanford California USA.
Jayakar V NayakDepartment of Otolaryngology-Head and Neck Surgery Stanford University School of Medicine Stanford California USA.
Peter H HwangDepartment of Otolaryngology-Head and Neck Surgery Stanford University School of Medicine Stanford California USA.
Zara M PatelDepartment of Otolaryngology-Head and Neck Surgery Stanford University School of Medicine Stanford California USA.ORCID 0000-0003-2072-982X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To analyze the accuracy of ChatGPT-generated responses to common rhinologic patient questions. Methods: Ten common questions from rhinology patients were compiled by a panel of 4 rhinology fellowship-trained surgeons based on clinical patient experience. This panel (Panel 1) developed consensus "expert" responses to each question. Questions were individually posed to ChatGPT (version 3.5) and its responses recorded. ChatGPT-generated responses were individually graded by Panel 1 on a scale of 0 (incorrect) to 3 (correct and exceeding the quality of expert responses). A 2nd panel was given the consensus and ChatGPT responses to each question and asked to guess which response corresponded to which source. They then graded ChatGPT responses using the same criteria as Panel 1. Question-specific and overall mean grades for ChatGPT responses, as well as interclass correlation coefficient (ICC) as a measure of interrater reliability, were calculated. Results: The overall mean grade for ChatGPT responses was 1.65/3. For 2 out of 10 questions, ChatGPT responses were equal to or better than expert responses. However, for 8 out of 10 questions, ChatGPT provided responses that were incorrect, false, or incomplete based on mean rater grades. Overall ICC was 0.526, indicating moderate reliability among raters of ChatGPT responses. Reviewers were able to discern ChatGPT from human responses with 97.5% accuracy. Conclusion: This preliminary study demonstrates overall near-complete and variably accurate responses provided by ChatGPT to common rhinologic questions, demonstrating important limitations in nuanced subspecialty fields.

Indexed as

artificial intelligencechatboteducation technologymachine learningneural networkpatient educationrhinology

Identifiers

PMID39776758
PMCPMC11705442

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.