Mini-abstract: The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data.
Objective: Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice.
Summary background data: The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies.
Methods: The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm.
Results and conclusions: The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.
Keywords: Artificial intelligence; Big data; Machine learning.
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