Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Medical Sciences Division, University of Oxford, Oxford, OX3 7LD, UK John Radcliffe Hospital, Headley Way, Headington, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Int J Surg. 2021 Oct;94:106133. doi: 10.1016/j.ijsu.2021.106133. Epub 2021 Sep 29.
The exponential increase in the volume and complexity of healthcare data presents new challenges to researchers and clinicians in analysis and interpretation. The requirement for new strategies to extract meaningful information from large, noisy datasets has led to the development of the field of big data analytics. Artificial intelligence (AI) is a general-purpose technology in which machines carry out tasks traditionally thought to be only achievable by humans. Machine learning (ML) is an approach to AI in which machines can "learn" to perform tasks in an automated process, rather than being explicitly programmed by a human. Research aiming to apply ML techniques to classification, prediction and decision-making problems in healthcare has increased 61-fold from 2005 to 2019, mirroring this sense of early promise. The field of healthcare ML is relatively young, and many critical steps are needed before adoption into clinical practice, including transparent, unbiased development and reporting of algorithms. Articles claiming that machines can outperform, or replace, doctors in high-level tasks, such as diagnosis or prognostication, must be carefully appraised. It is critical that surgeons have an understanding of the principles and terminology of AI and ML to evaluate these claims and to take an active role in directing research. This article is an up-to-date review and primer for surgeons covering the core tenets of ML applied to surgical problems, including algorithm types and selection, model training and validation, interpretation of common outcome metrics, current and future reporting guidelines and discussion of the challenges and limitations in this field.
医疗保健数据的数量和复杂性呈指数级增长，这给研究人员和临床医生在分析和解释方面带来了新的挑战。需要新的策略来从大型嘈杂数据集提取有意义的信息，这导致了大数据分析领域的发展。人工智能 (AI) 是一种通用技术，机器可以执行传统上认为只能由人类完成的任务。机器学习 (ML) 是 AI 的一种方法，其中机器可以在自动化过程中“学习”执行任务，而无需由人类明确编程。旨在将 ML 技术应用于医疗保健分类、预测和决策问题的研究从 2005 年到 2019 年增长了 61 倍，反映了这种早期的希望。医疗保健 ML 领域相对较年轻，在将其应用于临床实践之前需要许多关键步骤，包括算法的透明、无偏开发和报告。声称机器可以在高级任务（如诊断或预后）中超越或取代医生的文章必须仔细评估。外科医生必须了解 AI 和 ML 的原理和术语，以评估这些说法，并在指导研究方面发挥积极作用。本文是一篇针对外科医生的最新综述和入门文章，涵盖了应用于外科问题的 ML 的核心原则，包括算法类型和选择、模型训练和验证、常见结果指标的解释、当前和未来的报告指南以及讨论该领域的挑战和限制。
