Machine Learning in Surgery: Big Data

This chapter delves into the intersection of machine learning and surgery, tracing the historical evolution of artificial intelligence (AI) in medicine. Machine learning, described as an algorithmic field blending statistics and computer science, focuses on prediction rather than inference. The narrative explains the transition from classical statistics, rooted in inference, to the machine learning paradigm, emphasizing the pivotal difference between prediction and inference. The chapter elucidates the mechanics of machine learning, detailing algorithms, their evaluation parameters and the significance of choosing an appropriate model. It explores the application of AI in medical diagnostics, particularly in image-based predictions. Additionally, the chapter highlights the challenges, opportunities, and ethical considerations in incorporating machine learning into surgical research, emphasizing the crucial role of quality data in overcoming the ‘Big Data Paradox’ for meaningful outcomes in precision medicine.