Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery
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.
Authors
- Jon‐Paul Pepper (ORCID: https://orcid.org/0000-0001-9416-9170)
- Nikolas H. Blevins (ORCID: https://orcid.org/0000-0003-3562-4887)
- Philip C. Doyle (ORCID: https://orcid.org/0000-0002-2715-3619)
- Minsik Lee (ORCID: https://orcid.org/0000-0003-4941-4311)
- Noel Ayoub (ORCID: https://orcid.org/0000-0003-1867-994X)
- Francis X. Creighton (ORCID: https://orcid.org/0000-0003-3131-1162)
- C. Kwang Sung (ORCID: https://orcid.org/0000-0001-9795-6452)
- Soraya Fereydooni (ORCID: https://orcid.org/0000-0002-1264-6461)
- George S. Liu (ORCID: https://orcid.org/0000-0002-2066-5734)
- Robson Capasso (ORCID: https://orcid.org/0000-0003-2645-1793)
- Michael T. Chang (ORCID: https://orcid.org/0000-0001-9519-5880)
- Konstantina M. Stanković (ORCID: https://orcid.org/0000-0003-0233-279X)
- Zara M. Patel (ORCID: https://orcid.org/0000-0003-2072-982X)
- Mihir M. Shah
- Jeffrey Huynh
- F. Christopher Holsinger
- Srinidhi Polkampally
- Sravya Kuchibhotla
Institutions
- Johns Hopkins University (US)
- Stanford University (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-05-10
- DOI
- https://doi.org/10.1038/s41746-025-01693-0
- Citations
- 11
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 7.20
Funders
- Fondation Bertarelli