In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.

过去十年，基于人工智能 (AI) 的应用在医疗保健领域呈爆炸式增长。在心血管疾病，特别是血管外科领域，机器学习、自然语言处理和深度神经网络等人工智能工具已被应用于自动检测未确诊的疾病，如外周动脉疾病、腹主动脉瘤和动脉粥样硬化性心血管疾病。除了疾病检测和风险分层之外，人工智能还被用来确定符合指南的他汀类药物治疗的使用和不使用的原因，这对基于人群的心血管疾病健康具有重要影响。尽管许多研究强调了人工智能的潜在应用，但很少有研究涉及可用的基于人工智能的工具的真正临床工作流程实施。具体例子，例如根据患者个体风险因素确定最佳他汀类药物治疗以及增强术中透视和超声成像，证明了人工智能融入临床工作流程的潜在前景。人工智能在医疗保健领域的实施仍然面临许多挑战，包括数据互操作性、模型偏差和普遍性、前瞻性评估、隐私和安全以及监管。多学科和多机构合作以及采用集成框架对于将人工智能工具成功应用于临床实践至关重要。