Evidence map›Paper›PMID 41783517›Full record

ArticleOTO open

How Easily Can AI Chatbots Spread Misinformation in Audiology and Otolaryngology?

W Wiktor Jedrzejczak, Agata Szkiełkowska, Danuta Raj-Koziak, Elżbieta Włodarczyk, Henryk Skarżyński, Krzysztof Kochanek

Abstract read
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. Reference Accuracy in Large Language Model Chatbots: A Metric for Inherent Misinformation?Medical science monitor : international medical journal of experimental and clinical research · 2026
    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

6 authors.

W Wiktor JedrzejczakDepartment of Experimental Audiology World Hearing Center, Institute of Physiology and Pathology of Hearing Warsaw Poland.ORCID https://orcid.org/0000-0001-8404-0672
Agata SzkiełkowskaAudiology and Phoniatrics Clinic World Hearing Center, Institute of Physiology and Pathology of Hearing Warsaw Poland.ORCID https://orcid.org/0000-0003-2122-7872
Danuta Raj-KoziakTinnitus Department World Hearing Center, Institute of Physiology and Pathology of Hearing Warsaw Poland.ORCID https://orcid.org/0000-0002-4067-6043
Elżbieta WłodarczykRehabilitation Clinic World Hearing Center, Institute of Physiology and Pathology of Hearing Warsaw Poland.ORCID https://orcid.org/0000-0003-3737-5730
Henryk SkarżyńskiOtorhinolaryngosurgery Clinic World Hearing Center, Institute of Physiology and Pathology of Hearing Warsaw Poland.ORCID https://orcid.org/0000-0001-7141-9851
Krzysztof KochanekDepartment of Experimental Audiology World Hearing Center, Institute of Physiology and Pathology of Hearing Warsaw Poland.ORCID https://orcid.org/0000-0003-2860-5784

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Chatbots powered by large language models (LLMs) have recently emerged as prominent sources of information. However, their ability to propagate misinformation as well as information, particularly in specialized fields like audiology and otolaryngology, remains underexplored. This study aimed to evaluate the accuracy of 6 popular chatbots-ChatGPT, Gemini, Claude, DeepSeek, Grok, and Mistral-in response to questions framed around a range of unproven methods in audiological and otolaryngological care. Study Design: Cross-sectional study. Setting: A set of 50 questions was developed based on common conversations between patients and clinicians. We then posed these questions to the chatbots. Methods: We tested each chatbot 10 times to account for variable responses, producing a total of 3000 responses. The responses were compared with correct answers based on the general opinion of 11 professionals. The consistency of the responses was evaluated by Cohen's Kappa. Results: Most chatbot responses to the majority of questions were deemed accurate. Grok consistently performed best, where its answers aligned perfectly with the opinions of the experts. Deepseek exhibited the lowest accuracy, scoring 95.8%. Mistral exhibited the lowest consistency, scoring 0.96. Conclusions: Although all the chatbots generally avoided endorsing unproven methods, some responses deviated from the expert consensus and could therefore be said to contribute to the spread of misinformation. The best performer among the group was Grok, which provided consistently accurate responses, showing it has the potential for clinical and educational use within audiology and otolaryngology.

Indexed as

audiologyChatGPTClaudeDeepSeekGeminiGrokmisinformationmistralotolaryngology

Identifiers

PMID41783517
PMCPMC12955831

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.