Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI’s applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns—like data privacy and algorithmic bias—must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.
人工智能（AI）正通过提升诊断准确性、操作效率和临床结果，革新胰胆内镜领域。本文综述探讨了AI在内镜超声（EUS）和内镜逆行胰胆管造影（ERCP）中的应用，重点分析其解决胰胆疾病诊断与治疗难题的潜力。在EUS中，AI通过提升胰腺肿块鉴别、恶性病变预测及解剖标志识别能力，展现出优于传统指南的高诊断准确率。在ERCP领域，AI通过实时数据整合实现精准胆道狭窄识别、优化操作技术并辅助临床决策，显著改善乳头辨识度及插管难度预测。此外，预测分析模型有助于降低ERCP术后胰腺炎等并发症风险。AI在胰胆内镜的未来发展将聚焦多模态数据融合，整合影像学、基因组学及分子数据以推动精准医疗。然而，为确保安全应用，仍需应对数据质量、外部验证、临床医师培训等挑战，并关注数据隐私、算法偏见等伦理问题。通过突破这些瓶颈，AI有望重塑胰胆疾病诊疗体系，实现诊断精准化、治疗高效化及医疗个性化。
Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease
肿瘤(癌症)患者之家