Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications

Abstract

Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.

Keywords: artificial intelligence; calculator; deep learning; machine learning; risk assessment; surgical complications.

Figure 1.

Linear vs nonlinear models. (A) Linear models assume that variables interact in a linear and additive manner and therefore over- or underestimate risks at the extreme ranges of variables. (B) Nonlinear models assume that the interaction of patient demographics, comorbidities, and surgical factors is far from linear and that certain variables gain or lose relevance as a function of the presence or absence of other variables, better representing the interaction of risk factors in real life.