**Abstract**

Artificial intelligence (AI) is an umbrella term, which refers to different methods that simulate the process of human learning. As is the case with medicine in general, the field of bariatric metabolic surgery has lately been overwhelmed by evidence relevant to the applications of AI in numerous aspects of its clinical practice, including prediction of complications, effectiveness for weight loss and remission of associated medical problems, improvement of quality of life, intraoperative features, and cost-effectiveness. Current studies are highly heterogeneous regarding their datasets, as well as their metrics and benchmarking, which has a direct impact on the quality of research. For the non-familiar clinician, AI should be deemed as a novel statistical tool, which, in contradistinction to traditional statistics, draws their source data from real-world databases and registries rather than idealized cohorts of patients and is capable of managing vast amounts of data. This way, AI is supposed to support decision-making rather than substitute critical thinking or surgical skill development. As with any novelty, the clinical usefulness of AI remains to be proven and validated against established methods.

**Keywords:** artificial intelligence, machine learning, deep learning, data mining, decision trees, bariatric surgery, metabolic surgery, obesity, diabetes mellitus, obesity-related health problems, surgical safety, effectiveness, quality of life
