Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …

Background Machine learning (ML) methods are increasingly used in addition to
conventional statistical modelling (CSM) for predicting readmission and mortality in patients …

Artificial intelligence (AI) centred diagnostic systems are increasingly recognised as robust
solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in …

Reperfusion therapy after acute myocardial infarction can induce myocardial ischemia-
reperfusion injury (IRI). Novel evidence has illustrated that N6-methyladenosine (m6A) …

Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate
human intelligence. Machine learning is one of them, and deep learning is one of those …

Background and objective Acute respiratory distress syndrome (ARDS) is a life-threatening
pulmonary disease with a high clinical and cost burden across the globe. Artificial …

Artificial intelligence demonstrates the ability to learn through assimilation of large datasets
to unravel complex relationships, discover prior unfound pathophysiological states and …

Objective To evaluate the diagnostic performance of machine learning (ML) in predicting
lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors …

Background Current electrocardiogram analysis algorithms cannot predict the presence of
coronary artery disease (CAD), especially in stable patients. This study assessed the ability …

Background Artificial intelligence (AI) has potential uses in healthcare including the
detection of health conditions and prediction of health outcomes. Past systematic reviews …