The present review discusses the transformative role of AI in the diagnosis and management of head and neck cancers (HNCs).Methods:It explores how AI technologies, including ML, DL, and CNNs, are applied in various diagnostic tasks, such as medical imaging, molecular profiling, and predictive modeling.Results:This review highlights AI’s ability to improve diagnostic accuracy and efficiency, particularly in analyzing medical images like CT, MRI, and PET scans, where AI sometimes outperforms human radiologists. This paper also emphasizes AI’s application in histopathology, where algorithms assist in whole-slide image (WSI) analysis, tumor-infiltrating lymphocytes (TILs) quantification, and tumor segmentation. AI shows promise in identifying subtle or rare histopathological patterns and enhancing the precision of tumor grading and treatment planning. Furthermore, the integration of AI with molecular and genomic data aids in mutation analysis, prognosis, and personalized treatment strategies.Conclusions:Despite these advancements, the review identifies challenges in AI adoption, such as data standardization and model interpretability, and calls for further research to fully integrate AI into clinical practice for improved patient outcomes.
本综述探讨了人工智能在头颈癌诊断与管理中的变革性作用。方法：研究分析了包括机器学习、深度学习和卷积神经网络在内的人工智能技术如何应用于医学影像、分子谱分析和预测建模等多种诊断任务。结果：综述强调人工智能在提升诊断准确性与效率方面的能力，尤其在分析CT、MRI和PET等医学影像时，其表现有时甚至超越人类放射科医生。本文还着重阐述了人工智能在组织病理学中的应用，算法可辅助全切片图像分析、肿瘤浸润淋巴细胞定量及肿瘤分割。人工智能在识别细微或罕见组织病理学模式、提升肿瘤分级与治疗计划精确性方面展现出潜力。此外，人工智能与分子及基因组数据的整合有助于突变分析、预后评估及个体化治疗策略制定。结论：尽管取得这些进展，本综述指出人工智能临床应用仍面临数据标准化与模型可解释性等挑战，并呼吁开展进一步研究以推动人工智能全面融入临床实践，从而改善患者预后。
肿瘤(癌症)患者之家