Liu George S, Fereydooni Soraya, Lee Melissa Chaehyun, Polkampally Srinidhi, Huynh Jeffrey, Kuchibhotla Sravya, Shah Mihir M, Ayoub Noel F, Capasso Robson, Chang Michael T, Doyle Philip C, Holsinger F Christopher, Patel Zara M, Pepper Jon-Paul, Sung C Kwang, Creighton Francis X, Blevins Nikolas H, Stankovic Konstantina M
Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA.
NPJ Digit Med. 2025 May 10;8(1):265. doi: 10.1038/s41746-025-01693-0.
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.
临床验证研究对于将人工智能（AI）技术应用于医疗保健领域至关重要，但在耳鼻咽喉头颈外科（OHNS）中可能表现不佳。本综述研究了1996年至2023年间OHNS领域的深度学习出版物。通过检索MEDLINE、EMBASE和科学网数据库，共识别出3236篇文章，其中444篇符合纳入标准。2012年至2022年期间，来自48个国家的出版物呈指数级增长，且大多集中在耳科学和神经耳科学领域（28%），最主要的目标是扩展医疗保健提供者的能力（56%），最常使用图像输入数据（55%）和卷积神经网络模型（63%）。令人惊讶的是，几乎所有研究（99.3%）都是计算机模拟的概念验证早期研究。三项研究（0.7%）进行了离线验证，零项研究（0%）进行了临床验证，这揭示了OHNS领域的“AI鸿沟”。跨越这一鸿沟的建议包括专注于低复杂性和低风险任务、遵循报告指南以及优先开展临床转化研究。
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