Introduction: Cases of throat cancer are rising worldwide. With survival decreasing significantly at later stages, early detection is vital. Artificial intelligence (AI) and machine learning (ML) have the potential to detect throat cancer from patient speech, facilitating earlier diagnosis and reducing the burden on overstretched healthcare systems. However, no comprehensive review has explored the use of AI and ML for detecting throat cancer from speech. This review aims to fill this gap by evaluating how these technologies perform and identifying issues that need to be addressed in future research. Materials and Methods: We conducted a scoping literature review across three databases: Scopus, Web of Science, and PubMed. We included articles that classified speech using machine learning and specified the inclusion of throat cancer patients in their data. Articles were categorized based on whether they performed binary or multi-class classification. Results: We found 27 articles fitting our inclusion criteria, 12 performing binary classification, 13 performing multi-class classification, and two that do both binary and multiclass classification. The most common classification method used was neural networks, and the most frequently extracted feature was mel-spectrograms. We also documented pre-processing methods and classifier performance. We compared each article against the TRIPOD-AI checklist, which showed a significant lack of open science, with only one article sharing code and only three using open-access data. Conclusion: Open-source code is essential for external validation and further development in this field. Our review indicates that no single method or specific feature consistently outperforms others in detecting throat cancer from speech. Future research should focus on standardizing methodologies and improving the reproducibility of results.

翻译：引言：全球喉癌病例持续上升。由于晚期生存率显著下降，早期检测至关重要。人工智能（AI）与机器学习（ML）具备通过患者语音检测喉癌的潜力，有助于实现更早诊断并减轻过度紧张的医疗系统负担。然而，目前尚无综合性综述系统探讨利用AI与ML从语音检测喉癌的研究。本综述旨在填补这一空白，通过评估这些技术的性能表现并指出未来研究中需解决的问题。材料与方法：我们在Scopus、Web of Science和PubMed三个数据库中进行了范围性文献综述。纳入标准为使用机器学习对语音进行分类并明确说明数据包含喉癌患者的研究文献。根据是否执行二分类或多分类任务对文献进行归类。结果：共发现27篇符合纳入标准的文献，其中12篇进行二分类，13篇进行多分类，另有2篇同时包含两种分类方式。最常用的分类方法是神经网络，最常提取的特征为梅尔频谱图。我们还系统记录了预处理方法与分类器性能。通过TRIPOD-AI清单对每篇文献进行评估，结果显示该领域存在严重的开放科学缺失问题——仅有一篇文献公开代码，仅三篇使用开放获取数据。结论：开源代码对于该领域的外部验证与持续发展至关重要。本综述表明，目前尚无单一方法或特定特征在语音喉癌检测中持续优于其他方案。未来研究应致力于方法学标准化并提升结果的可复现性。