Futurists have anticipated that novel autonomous technologies, embedded with machine
learning (ML), will substantially influence healthcare. ML is focused on making predictions …

Background: Clinical artificial intelligence (AI) has reached a critical inflection point.
Advances in algorithmic science and increased understanding of operational considerations …

Background: The utilization of artificial intelligence and machine learning as diagnostic and
predictive tools in perioperative medicine holds great promise. Indeed, many studies have …

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 …

Background Conventional statistics are based on a simple cause-and-effect principle.
Postoperative complications, however, have a multifactorial and interrelated etiology. The …

Purpose To develop and validate machine learning (ML) models for cancer-associated deep
vein thrombosis (DVT) and to compare the performance of these models with the Khorana …

Background Risk stratification plays a central role in anesthetic evaluation. The use of Big
Data and machine learning (ML) offers considerable advantages for collection and …

Venous thromboembolism (VTE), which encompasses deep venous thrombosis (DVT) and
pulmonary embolism (PE), is associated with significant mortality and morbidity among …

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can
lead to considerable morbidity, mortality, and additional costs. However, to date, there is no …

The application of artificial intelligence technologies is growing in several fields of
healthcare settings. The aim of this article is to review the current applications of artificial …