Artificial Intelligence Models for Dysphonia Patient Education

Abstract

Objective: Evaluate and compare artificial intelligence (AI) chatbot responses to queries extrapolated from statements by Dysphonia International (DI). Study Design: Cross-sectional analysis. Setting: Online using ChatGPT, Google AI Overview (GAO), and DI. Methods: Two 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