Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

Chronic diseases are becoming more widespread. Treatment and monitoring of these
diseases require going to hospitals frequently, which increases the burdens of hospitals and …

Intensive-care clinicians are presented with large quantities of measurements from multiple
monitoring systems. The limited ability of humans to process complex information hinders …

Background Many mortality prediction models have been developed for patients in intensive
care units (ICUs); most are based on data available at ICU admission. We investigated …

Background: Successful integrations of machine learning into routine clinical care are
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …

Although numerous ethical principles and guidelines have been proposed to guide the
development of artificial intelligence (AI) systems, it has proven difficult to translate these …

Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early
prediction of AKI could prompt preventive measures, but is challenging in the clinical routine …

Introduction Early detection and intervention of atrial fibrillation (AF) is a cornerstone for
effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have …

Artificial intelligence (AI) is gradually changing the practice of surgery with technological
advancements in imaging, navigation, and robotic intervention. In this article, we review the …

Importance Postoperative complications can significantly impact perioperative care
management and planning. Objectives To assess machine learning (ML) models for …