Evidence map›Paper›PMID 40346307›Full record

ArticleNPJ digital medicine2025

Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery.

George S Liu, Soraya Fereydooni, Melissa Chaehyun Lee, Srinidhi Polkampally, Jeffrey Huynh, Sravya Kuchibhotla, Mihir M Shah, Noel F Ayoub, Robson Capasso, Michael T Chang and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Turing problems in otolaryngology: a scoping review of the principal challenges of artificial ıntelligence and large language models.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
    Review
  2. Article
  3. Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026
    Review
  4. Article
  5. Review
  6. Article
  7. The Utilization of Artificial Intelligence by Pediatric Otolaryngology Surgeons in Professional Practice.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale
    Article
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

18 authors.

George S LiuDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA. gliu51@jh.edu.
Soraya FereydooniDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Melissa Chaehyun LeeDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Srinidhi PolkampallyDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Jeffrey HuynhDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Sravya KuchibhotlaDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Mihir M ShahDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Noel F AyoubDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Robson CapassoDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Michael T ChangDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Philip C DoyleDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
F Christopher HolsingerDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Zara M PatelDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Jon-Paul PepperDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
C Kwang SungDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Francis X CreightonDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA.
Nikolas H BlevinsDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Konstantina M StankovicDepartment of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical validation studies are important to translate artificial intelligence (AI) technology in healthcare but may be underperformed in Otolaryngology - Head & Neck Surgery (OHNS). This scoping review examined deep learning publications in OHNS between 1996 and 2023. Searches on MEDLINE, EMBASE, and Web of Science databases identified 3236 articles of which 444 met inclusion criteria. Publications increased exponentially from 2012-2022 across 48 countries and were most concentrated in otology and neurotology (28%), most targeted extending health care provider capabilities (56%), and most used image input data (55%) and convolutional neural network models (63%). Strikingly, nearly all studies (99.3%) were in silico, proof of concept early-stage studies. Three (0.7%) studies conducted offline validation and zero (0%) clinical validation, illuminating the "AI chasm" in OHNS. Recommendations to cross this chasm include focusing on low complexity and low risk tasks, adhering to reporting guidelines, and prioritizing clinical translation studies.

Identifiers

PMID40346307
PMCPMC12064819

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Registered trials

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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.