Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine.
Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York, USA.
Curr Opin Otolaryngol Head Neck Surg. 2024 Dec 1;32(6):391-397. doi: 10.1097/MOO.0000000000000999. Epub 2024 Jul 24.
The purpose of this review is to summarize the existing literature on artificial intelligence technology utilization in laryngology, highlighting recent advances and current barriers to implementation.
The volume of publications studying applications of artificial intelligence in laryngology has rapidly increased, demonstrating a strong interest in utilizing this technology. Vocal biomarkers for disease screening, deep learning analysis of videolaryngoscopy for lesion identification, and auto-segmentation of videofluoroscopy for detection of aspiration are a few of the new ways in which artificial intelligence is poised to transform clinical care in laryngology. Increasing collaboration is ongoing to establish guidelines and standards for the field to ensure generalizability.
Artificial intelligence tools have the potential to greatly advance laryngology care by creating novel screening methods, improving how data-heavy diagnostics of laryngology are analyzed, and standardizing outcome measures. However, physician and patient trust in artificial intelligence must improve for the technology to be successfully implemented. Additionally, most existing studies lack large and diverse datasets, external validation, and consistent ground-truth references necessary to produce generalizable results. Collaborative, large-scale studies will fuel technological innovation and bring artificial intelligence to the forefront of patient care in laryngology.
本文旨在总结目前在喉科学中应用人工智能技术的文献，强调其近期进展和目前实施的障碍。
研究人工智能在喉科学中应用的文献数量迅速增加，表明人们对利用这项技术有着浓厚的兴趣。用于疾病筛查的声学生物标志物、用于病变识别的视频喉镜的深度学习分析，以及用于检测吸入的视频透视术的自动分割，都是人工智能有望改变喉科学临床护理的几个新方法。目前正在进行更多的合作，以制定该领域的指南和标准，以确保其可推广性。
人工智能工具具有通过创建新的筛查方法、改善对喉科学大量数据的诊断分析以及标准化结果测量来极大地推进喉科学护理的潜力。然而，为了成功实施人工智能，医生和患者对其的信任度必须提高。此外，大多数现有的研究缺乏大的、多样化的数据集、外部验证以及产生可推广结果所需的一致的真实参考。合作性的、大规模的研究将推动技术创新，并使人工智能成为喉科学患者护理的前沿。
