ArticleWorld journal of otorhinolaryngology - head and neck surgery2026
Blinded by the Bot: Benchmarking GPT and Gemini Against Human Authors in Otolaryngology Reviews.
Article in World journal of otorhinolaryngology - 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare the quality of scientific review articles generated by two artificial intelligence systems, ChatGPT and Gemini, with those written by human authors in the field of otolaryngology. Methods: Two otolaryngology topics, chronic rhinosinusitis and infantile subglottic hemangioma, were selected. For each topic, four AI-generated reviews (GPT-4.0 and Gemini 2.0; narrative and PRISMA-style) and one human-authored peer-reviewed review were included, yielding a total of 10 manuscripts (8 AI-generated, 2 human-authored). A blinded panel of seven board-certified otolaryngologists evaluated all manuscripts using a 5-point Likert scale across seven domains: scientific accuracy, depth of content, citation quality, structure and organization, readability and tone, critical insight, and overall scientific quality. Group comparisons were performed using linear mixed-effects models with random intercepts for reviewer and manuscript. Interrater reliability was assessed using Shrout-Fleiss intraclass correlation coefficients (ICC). Manual verification of AI-generated references was conducted to assess citation accuracy and fabrication. Results: Human-authored manuscripts received the highest ratings across all domains (overall quality 4.50 ± 0.76). GPT-4.0 demonstrated moderate performance (2.71 ± 1.46 overall), while Gemini 2.0 scored lowest (2.14 ± 1.01). Mixed-effects modeling demonstrated significant group differences across all domains ( Conclusion: GPT generated fluent, stylistically strong reviews but remained significantly inferior to human-authored manuscripts in analytical depth and citation integrity. Gemini 2.0 underperformed across all domains and demonstrated a substantial rate of fabricated citations. As large language models become integrated into academic workflows, transparent disclosure, structured fact-checking, and human oversight remain essential to safeguard scientific reliability.
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.