Comparative Evaluation of ChatGPT and Gemini Responses to Vertigo-Related Questions: Accuracy, Information Quality, and Readability

This study was designed as a cross-sectional methodological analysis to evaluate the accuracy, quality, and readability of responses generated by large language models (ChatGPT and Gemini) to frequently asked questions about vertigo. A total of 50 questions were initially generated from three sources: ChatGPT, Gemini, and Google’s “People also ask” section. After removing duplicates and irrelevant items, 20 representative questions were selected.
Each question was entered into both models, and only the first response was recorded without any follow-up prompts. The responses were evaluated by five blinded experts (two otolaryngologists, two audiologists, and one physiotherapist). Medical accuracy was assessed using a 4-point Likert scale, and information quality was evaluated using the DISCERN instrument. Mean scores across experts were used for analysis.
Readability was assessed using multiple standard indices, including Flesch Reading Ease, Flesch–Kincaid Grade Level, Gunning Fog Index, SMOG, Coleman–Liau Index, and the Automated Readability Index. Additional textual features such as word count, sentence number, average sentence length, and percentage of complex words were also analyzed to determine linguistic complexity.