Evidence map›Paper›PMID 42120572›Full record

ArticleEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2026

Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes.

William Pennington-FitzGerald, Akshay Warrier, Sally Durant, Ibraheem Sharaf, Francesca Carlino, Sai Dheeraj Pamula, Jean Anderson Eloy, Jessica Levi

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Article in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - 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.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

William Pennington-FitzGeraldDepartment of Otolaryngology-Head and Neck Surgery, Boston University Medical Center, One Boston Medical Center Pl, Boston, MA, 02118, USA. william.pennington-fitzgerald@bmc.org.ORCID http://orcid.org/0000-0003-3706-2005
Akshay WarrierDepartment of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA.
Sally DurantDepartment of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA.
Ibraheem SharafDepartment of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA.
Francesca CarlinoDepartment of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA.
Sai Dheeraj PamulaDepartment of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA.
Jean Anderson EloyDepartment of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA.
Jessica LeviDepartment of Otolaryngology-Head and Neck Surgery, Boston University Medical Center, One Boston Medical Center Pl, Boston, MA, 02118, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to compare the diagnostic accuracy and citation integrity of four large language models (LLMs) including one general (ChatGPT-4) and three intended for clinical and research use (OpenEvidence, Perplexity, and Pathway), using standardized otolaryngology clinical vignettes.

methodsOne hundred validated otolaryngology clinical vignettes were presented to each LLM with a prompt requesting both a diagnosis and supporting citations. Diagnostic accuracy was determined against reference answers, and errors were categorized as logical, informational, or explicit. Citation number, source type, hallucination rate, and journal CiteScores were also compared.

resultsAll models demonstrated high diagnostic accuracy (82.0%-91.0%), with ChatGPT-4 achieving the highest numerical accuracy (91.0%), though differences between models were not statistically significant (p = 0.057). Logical errors were most frequent across all models. OpenEvidence and Perplexity generated the most citations per response, while ChatGPT-4 produced the fewest and had the highest hallucination rate (23.0%). Source preferences varied, with OpenEvidence and Pathway favoring narrative reviews and Perplexity favoring government/public health websites. OpenEvidence had the highest mean CiteScore for journal citations.

conclusionThis is the first study to assess both diagnostic accuracy and citation integrity of LLMs in otolaryngology. While ChatGPT-4 was most accurate, it had the highest rate of citation hallucinations, suggesting a trade-off between accuracy and source reliability. OpenEvidence, though slightly less accurate, provided more consistent and verifiable references, demonstrating the ability to prioritize citation integrity alongside diagnostic performance for clinical integration.

Indexed as

Large Language ModelsOtolaryngologyHumansArtificial IntelligenceChatGPTCitationDiagnostic AccuracyHallucinationLarge Language ModelOpenEvidenceOtolaryngologyPathwayPerplexity

Identifiers

PMID42120572

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.