Evidence map›Paper›PMID 41084356›Full record

ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2025

Artificial Intelligence Models for Dysphonia Patient Education.

Rebecca An Ho, Esha Shah, John Sebastian De Armas, Kenneth Yan, Rachel Kaye

Abstract read
In one paragraph

Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2025. 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. Review
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

5 authors.

Rebecca An HoDepartment of Otolaryngology Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA.ORCID https://orcid.org/0009-0001-2728-722X
Esha ShahDepartment of Otolaryngology Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA.ORCID https://orcid.org/0009-0004-5319-7852
John Sebastian De ArmasDepartment of Otolaryngology Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA.ORCID https://orcid.org/0000-0001-6801-5012
Kenneth YanDepartment of Otolaryngology Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA.ORCID https://orcid.org/0000-0002-0601-8127
Rachel KayeDepartment of Otolaryngology Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA.ORCID https://orcid.org/0000-0002-9700-0132

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveEvaluate and compare artificial intelligence (AI) chatbot responses to queries extrapolated from statements by Dysphonia International (DI). STUDY

designCross-sectional analysis.

settingOnline using ChatGPT, Google AI Overview (GAO), and DI.

methodsTwo board-certified otolaryngologists and an otolaryngology resident each blindly rated ChatGPT, GAO, and DI responses to spasmodic dysphonia, muscle tension dysphonia, and vocal tremor questions. Response comprehensiveness, accuracy, appropriateness to patients, and patient safety were graded on a 5-point Likert scale. Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Gunning Fog Index (GFI), and average word count assessed readability. Analysis was performed with one-way analysis of variance (ANOVA) and Tukey honestly significant difference (HSD); significance was set at P < .05.

resultsChatGPT was significantly more comprehensive than both GAO (P < .01) and DI (P = .0357). ChatGPT was significantly more accurate than both GAO (P < .01) and DI (P < .01), while GAO was more accurate than DI (P < .01). ChatGPT was found to be significantly more patient-friendly than both GAO (P < .01) and DI (P < .01). ChatGPT had significantly safer recommendations than GAO (P < .01). ChatGPT had a significantly higher FKGL than GAO (P < .01), and a lower FRES than both GAO (P < .01) and DI (P < .01), indicating a more difficult reading level. All resources exceeded the recommended FKGL and GFI scores as well as the FRES of 90 to 100. Interrater reliability was 0.8.

conclusionChatGPT performed significantly better than GAO and DI regarding comprehensiveness, accuracy, appropriateness, and was an overall safer AI resource to use than GAO. All resources were above the recommended reading grade level. AI models prove to be a formidable alternative resource for patient-centered clinical information.

Indexed as

Artificial IntelligenceDysphoniaPatient Education as TopicCross-Sectional StudiesHumansartificial intelligencelaryngologypatient education

Identifiers

PMID41084356
PMCPMC12661483

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.