Affiliation:
1. Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
2. New York University Grossman School of Medicine, New York, NY, USA
Abstract
Abstract
OBJECTIVES
Machine learning (ML) has experienced a revolutionary decade with advances across many disciplines. We seek to understand how recent advances in ML are going to specifically influence the practice of surgery in the future with a particular focus on thoracic surgery.
METHODS
Review of relevant literature in both technical and clinical domains.
RESULTS
ML is a revolutionary technology that promises to change the way that surgery is practiced in the near future. Spurred by an advance in computing power and the volume of data produced in healthcare, ML has shown remarkable ability to master tasks that had once been reserved for physicians. Supervised learning, unsupervised learning and reinforcement learning are all important techniques that can be leveraged to improve care. Five key applications of ML to cardiac surgery include diagnostics, surgical skill assessment, postoperative prognostication, augmenting intraoperative performance and accelerating translational research. Some key limitations of ML include lack of interpretability, low quality and volumes of relevant clinical data, ethical limitations and difficulties with clinical implementation.
CONCLUSIONS
In the future, the practice of cardiac surgery will be greatly augmented by ML technologies, ultimately leading to improved surgical performance and better patient outcomes.
Publisher
Oxford University Press (OUP)
Subject
Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine,General Medicine,Surgery
Cited by
19 articles.
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