Department of Plastic & 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):11-19. doi: 10.1177/00031348221103648. Epub 2022 May 19.
Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.
人工智能（AI）专注于处理和解释复杂信息，以及识别复杂数据之间的关系和模式。人工智能和机器学习（ML）驱动的预测已经显示出在影响实时决策和改善手术结果方面的巨大潜力，因为它可以促进筛选、诊断、风险评估、术前规划和共同决策。对于 AI 在手术中的发展，对算法的基本理解，以及对其的开发和解释，都是至关重要的。在本文中，我们通过对常见的 ML 和深度学习算法、模型开发、性能指标和解释的概述，为外科医生提供了对 AI 驱动的预测模型的基本理解。这将成为理解基于 ML 的研究的基础，同时为进一步拓展这一新兴学科的应用提供新的思路和创新。
