Endoscopic ultrasound (EUS) effectively diagnoses malignant and pre-malignant gastrointestinal lesions. In the past few years, artificial intelligence (AI) has shown promising results in enhancing EUS sensitivity and accuracy, particularly for subepithelial lesions (SELs) like gastrointestinal stromal tumors (GISTs). Furthermore, AI models have shown high accuracy in predicting malignancy in gastric GISTs and distinguishing between benign and malignant intraductal papillary mucinous neoplasms (IPMNs). The utility of AI has also been applied to existing and emerging technologies involved in the performance and evaluation of EUS-guided biopsies. These advancements may improve training in EUS, allowing trainees to focus on technical skills and image interpretation. This review evaluates the current state of AI in EUS, covering imaging diagnosis, EUS-guided biopsies, and training advancements. It discusses early feasibility studies and recent developments, while also addressing the limitations and challenges. This article aims to review AI applications to EUS and its applications in clinical practice while addressing pitfalls and challenges.
超声内镜（EUS）能有效诊断胃肠道恶性及癌前病变。近年来，人工智能（AI）在提升EUS对胃肠道间质瘤（GISTs）等上皮下病变（SELs）的检测灵敏度与诊断准确性方面展现出显著潜力。AI模型在预测胃部GISTs恶性程度、鉴别胰腺导管内乳头状黏液性肿瘤（IPMNs）良恶性方面亦表现出较高准确性。此外，AI技术已应用于EUS引导下活检的操作实施与效果评估等现有及新兴技术领域。这些进展有望优化EUS培训体系，使学员能更专注于操作技能与影像判读能力的提升。本文综述了AI在EUS领域的应用现状，涵盖影像诊断、EUS引导活检及培训体系建设等方面，系统探讨了早期可行性研究及最新进展，同时剖析了当前存在的局限性与挑战。本文旨在全面评述AI在EUS临床实践中的应用价值，并深入分析其潜在风险与发展瓶颈。
肿瘤(癌症)患者之家