The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …

Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical
practice reveals an expanding interest in AI across clinical specialties and locations. The …

Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI
applications hold promise as tools to predict surgical outcomes, assess technical skills, or …

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …

Excellence in a wide variety of medical applications poses considerable challenges for AI,
requiring advanced reasoning, access to up-to-date medical knowledge and understanding …

Evaluating the performance of a clinical prediction model is crucial to establish its predictive
accuracy in the populations and settings intended for use. In this article, the first in a three …

Low-dose computer tomographic (LDCT) lung cancer screening reduces lung cancer–
specific and all-cause mortality among high-risk individuals, but implementation has been …

Background Multicentre training could reduce biases in medical artificial intelligence (AI);
however, ethical, legal, and technical considerations can constrain the ability of hospitals to …

Deep learning has been shown to accurately assess “hidden” phenotypes from medical
imaging beyond traditional clinician interpretation. Using large echocardiography datasets …