Perspectives: A surgeon's guide to machine learning

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.