本文系统探讨了人工智能（AI）技术在模拟医学培训中的应用现状、优势与挑战。AI通过虚拟患者系统、手术模拟评估、医学影像诊断培训及结构化报告优化四大核心场景,显著提升培训的智能化与个性化水平。研究表明,AI驱动的实时反馈机制（如手术技能评估系统）在随机对照试验中表现优于传统专家指导, 并具备大规模推广潜力, 可降低人力成本。然而,技术仍面临算法透明性、数据隐私伦理及临床转化效果验证等挑战。未来需深化跨学科合作, 结合增强现实（AR）等技术创新, 构建全球资源共享的智能认证体系, 推动医学教育范式转型。

       This review summarizes and discusses the application status, advantages,and challenges of artificial intelligence（AI）technology in simulated medical training．AI significantly enhances the intelligence and personalization of training through four core scenarios：virtual patient systems, surgical simulation assessment, medical imaging diagnosis training, and structured reporting optimization．Researches demonstrates that AI-driven real-time feedback mechanisms（e.g., surgical skill assessment systems）outperform traditional expert guidance in randomized controlled trials（P<0.001）and exhibit potential for large-scale implementation to reduce labor costs．However, challenges remain regarding algorithmic transparency, data privacy ethics, and clinical translation validation．Future efforts require deepened interdisciplinary collaboration, integration with innovations like augmented reality, and the establishment of a globally shared intelligent certification system to advance the transformation of medical education paradigms．

传统的结肠镜检查质量评估方式具有主观性强、费时费力等缺点。近年来,人工智能（AI）技术在结肠镜检查质量控制方面展现出客观性、即时性、全面性等优势,具有广阔的应用前景。文章全面探讨了AI在结肠镜检查质量控制中的多个应用场景,包括评估肠道准备质量、记录退镜时间、息肉识别和分类、预测早期结直肠癌浸润深度等方面,并通过具体的研究案例和数据分析了AI技术的有效性和优势。AI技术有望在提升结肠镜检查质量、促进结直肠癌的早诊早治方面发挥更加重要的作用,但面对技术、伦理及法规等多方面的挑战,未来需要持续努力,不断优化算法,加强跨学科合作,推动AI技术在医疗领域的健康、快速发展。

Traditional colonoscopy quality assessment methods have strong subjectivity and are time-consuming．In recent years,artificial intelligence technology has shown objectivity,timeliness,and comprehensiveness in colonoscopy quality control,with broad application prospects．This article comprehensively explores multiple application scenarios of AI in colonoscopy quality control,encompassing assessments of bowel preparation quality,recording of withdrawal times,polyp identification and classification,and prediction of early colorectal cancer invasion depth．Through specific research cases and data analysis,the effectiveness and advantages of AI technology are elucidated．AI technology is expected to play an increasingly significant role in enhancing the quality of colonoscopy and promoting early diagnosis and treatment of colorectal cancer．However,facing challenges from technology,ethics,regulations,and other aspects,continued efforts are needed in the future to continuously optimize algorithms,strengthen interdisciplinary collaboration,and promote the healthy and rapid development of AI technology in the medical field．

乳腺癌是女性最常见的原发恶性肿瘤之一。目前,通过采用综合治疗手段,包括手术、新辅助治疗、辅助放化疗等多种手段,乳腺癌已成为疗效最佳的实体肿瘤之一。其中,新辅助治疗（NAT）包括新辅助化疗、靶向治疗和内分泌治疗,目的是使肿瘤降期、保乳、保腋窝,并可观察药物敏感性,是当前乳腺癌综合治疗中非常重要的组成部分,其治疗疗效对患者手术方式的选择和预后至关重要。尽管病理学评价在评估NAT疗效方面被公认为金标准,但其局限性在于只能通过有创手段在治疗后进行,无法在治疗前对患者做出准确预测。磁共振成像（MRI）作为一项广泛使用的乳腺成像技术,在评估NAT疗效中扮演着关键角色。近年来,人工智能技术,尤其是影像组学（Radiomics）和深度学习（Deep Learning）,在医学影像分析领域取得了显著进展。这些技术能够从医学图像中提取大量肉眼难以识别的特征,揭示病变内部的微观结构和生物学行为,全面反映肿瘤的异质性,这不仅有助于临床医生更准确地区分良、恶性肿瘤,还能对恶性肿瘤的预后进行更为精确的评估。本文系统综述了近年来基于MRI的人工智能技术在乳腺癌新辅助治疗疗效评估中的应用研究,旨在促进人工智能在NAT临床实践中的应用和发展,为乳腺癌NAT治疗策略的优化和个性化医疗的实现提供科学依据。

Breast cancer is one of the most common primary malignant tumors in women．Currently,breast cancer has become one of the most effective solid tumors by using comprehensive treatment methods,including surgery,neoadjuvant therapy,adjuvant radiotherapy and chemotherapy．Among them,neoadjuvant therapy（NAT）,including neoadjuvant chemotherapy,targeted therapy and endocrine therapy,is a very important part of the current comprehensive treatment of breast cancer．It aims to reduce the tumor stage,preserve the breast,preserve the armpit,and observe the drug sensitivity．Its therapeutic effect is crucial to the choice of surgical methods and prognosis of patients．Although pathological evaluation is recognized as the gold standard in evaluating the response to NAT,its limitation is that it can only be performed after treatment by invasive means,and cannot accurately predict response before treatment．As a widely used breast imaging technology,magnetic resonance imaging（MRI）plays a key role in evaluating the response to NAT．However,traditional MRI evaluation methods are limited by the individual differences of interobserver and the low repeatability of evaluation results,which affects the accuracy of efficacy evaluation to a certain extent．In recent years,artificial intelligence technology,especially radiomics and deep learning,has made significant progress in the field of medical image analysis．These techniques can extract a large number of features that are difficult to be recognized by the naked eye from medical images,reveal the internal microstructure and biological behavior of the lesion,and fully reflect the heterogeneity of the tumor．This not only helps clinicians to distinguish benign and malignant tumors more accurately,but also makes a more accurate assessment of the prognosis of malignant tumors．This article reviews the application and progress of MRI-based artificial intelligence technology in evaluating the response to neoadjuvant therapy for breast cancer in the past five years,aiming to promote the application and development of artificial intelligence in NAT clinical practice,and provide a scientific basis for the optimization of NAT treatment strategy and the realization of personalized medicine for breast cancer．

       传统的结肠镜检查质量评估方式具有主观性强、费时费力等缺点。近年来，人工智能（AI）技术在结肠镜检查质量控制方面展现出客观性、即时性、全面性等优势，具有广阔的应用前景。文章全面探讨了AI在结肠镜检查质量控制中的多个应用场景，包括评估肠道准备质量、记录退镜时间、息肉识别和分类、预测早期结直肠癌浸润深度等方面，并通过具体的研究案例和数据分析了AI技术的有效性和优势。AI技术有望在提升结肠镜检查质量、促进结直肠癌的早诊早治方面发挥更加重要的作用，但面对技术、伦理及法规等多方面的挑战，未来需要持续努力，不断优化算法，加强跨学科合作，推动AI技术在医疗领域的健康、快速发展。

     Traditional colonoscopy quality assessment methods have strong subjectivity and are time-consuming．In recent years，artificial intelligence technology has shown objectivity，timeliness，and comprehensiveness in colonoscopy quality control，with broad application prospects．This article comprehensively explores multiple application scenarios of AI in colonoscopy quality control，encompassing assessments of bowel preparation quality，recording of withdrawal times，polyp identification and classification，and prediction of early colorectal cancer invasion depth．Through specific research cases and data analysis，the effectiveness and advantages of AI technology are elucidated．AI technology is expected to play an increasingly significant  role in enhancing the quality of colonoscopy and promoting early diagnosis and treatment of colorectal cancer．However，facing challenges from technology，ethics，regulations，and other aspects，continued efforts are  needed in the future to continuously optimize algorithms，strengthen interdisciplinary collaboration，and promote the healthy and rapid development of AI technology in the medical field．

       人工智能（AI）这一新兴技术的出现和应用给炎症性肠病（IBD）的诊断带来了巨大的变革。越来越多的研究着手于开发基于机器学习（ML）和深度学习（DL）的诊断模型，并获得了良好的诊断性能，尤其是在IBD的图像诊断，卷积神经网络（CNN）等模型由于其出色的图像分析能力，在内镜检查和组织病理检查等方面具有十分可观的发展前景。近年来AI诊断模型的应用越发广泛，但与此同时，关于算法、数据库及其应用方面仍存在一些难以忽视的局限性。本文将主要就图像识别方面对AI在IBD诊断中的应用进行综述，以期为IBD精准图像诊断领域下步研究提供参考。

        As an emerging technology，artificial intelligence（AI）has brought great changes to the precise diagnosis of inflammatory bowel disease（IBD）．More and more  researches have developed diagnostic models which are based on machine learning（ML）and deep learning（DL）and obtained satisfactory diagnostic performance．Especially in the image  diagnosis of IBD，convolutional neural network（CNN）and  other models  have  considerable  development  prospects in  endoscopy  and histopathology due to their excellent image analysis capabilities．In recent years，the application of AI diagnostic models has become more and more widespread，but at the same time，there are still some limitations about algorithms，databases and their applications that cannot be ignored．This review mainly focused on the application of AI in IBD diagnosis from the aspect of image recognition，to provide a reference for IBD diagnosis towards precision medicine．