Observational studyOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2026
Large Language Model Responses to Common Otolaryngological Questions: Evaluating Accuracy, Appropriateness, Readability, and Hallucinations.
Observational study in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtificial Intelligence (AI) is increasingly integrated into medicine, including otolaryngology. However, concerns remain regarding the accuracy of generated content and the tendency of large language models (LLMs) to fabricate references. This study evaluates the accuracy, appropriateness, readability, and hallucination of references in 2 prevalent large language models, ChatGPT and Claude, in response to common otolaryngological questions. STUDY
designProspective observational study. SETTINGS: Academic tertiary care center.
methodsThirty-six otolaryngologic questions were individually entered into ChatGPT 4.0 Plus and CLAUDE in separate sessions, with explicit instructions to avoid utilizing previous memories. To assess reproducibility, each query was submitted twice. Two otolaryngologists independently rated the accuracy of responses. Readability was evaluated using the Flesch Reading Ease (FRE) score. Reference hallucinations were assessed by analyzing the reference validity and relevance.
resultsChatGPT and CLAUDE had an FRE of 47 and 25.2 out of 100, respectively. For patient readability, ChatGPT scored a 3.60 while Claude scored a 4.68 out of 5. Claude scored slightly higher on accuracy, receiving a score of 4.42 out of 5 while ChatGPT received a 3.81. Both models hallucinated at least half of their references, with some citations irrelevant or incorrectly formatted. Thematic analysis revealed frequent vagueness, poor clinical prioritization, and excessive jargon across both models.
conclusionBoth ChatGPT and CLAUDE often produced partially inaccurate, jargon-filled responses and failed to consistently provide valid references when answering common otolaryngologic patient questions. Our results highlight the need for better understanding and regulation of LLM limitations in clinical and patient-facing applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.