Artificial Intelligence and Machine Learning for Risk Prediction in Surgery

Abstract

Artificial Intelligence (AI) has been a field of research for more than 70 years, with the goal of mimicking human thought processes in a computer. There were early successes in the subgenre of expert systems, designed to capture knowledge in specialist domains like medicine. These expert systems are part of a broader family of AI known as knowledge-based systems, which contain explicit knowledge expressed in human-readable form [1]. However, the current wave of excitement is largely driven by a different model, namely machine learning (ML). The idea is that by showing a computer algorithm thousands of examples of images or other forms of data, it will learn to associate those examples with their correct classification [1]. A key characteristic of ML is generalization. When presented with an image or data pattern that it has not seen before, the algorithm can classify it reliably, provided that similar examples existed in the training set. Unsurprisingly, many surgeons have limited knowledge of AI and ML. Nevertheless, the fusion of their experiences from the medical domain with those from the computing sciences has led to a significant interest in the developing discipline of health informatics.