Department of Plastic and Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA.
Am Surg. 2023 Jan;89(1):25-30. doi: 10.1177/00031348221101488. Epub 2022 May 13.
Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.
手术并发症给外科医生、患者和医疗保健系统带来了重大挑战,因为它们可能导致患者痛苦、治疗效果不佳和医疗保健成本增加。人工智能 (AI) 驱动的模型通过准确识别发生手术并发症风险较高的患者,并克服了传统基于统计学的风险计算器相关的几个局限性,彻底改变了外科领域。本文旨在概述使用常见机器学习和深度学习算法预测手术并发症的 AI,并说明如何将其用于术前对患者进行风险分层。这可以为未来基于患者个体因素的知情同意讨论提供基础。