Peak performance in surgery: how machine learning can improve surgical performance

Abstract

Surgical research is associated with specific methodological and practical challenges related to the assessment of complex interventions and variation of clinical equipoise. Biases can, to some degree, be overcome by randomisation, blinding, and intervention standardisation, although randomised trials can be costly and time-consuming. This poses a unique opportunity for innovation in surgical research design and evaluation to improve both validity of surgical trials and their assessment. There are many examples of challenging topics in surgical research, and despite thousands of studies, robust evidence is still elusive. Many of those challenges might be addressed by implementing Machine Learning approach.