Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives

Abstract

BACKGROUND: Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. METHODS: The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. RESULTS: The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. CONCLUSIONS: In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.

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