The pairing of massive data sets with processes—or algorithms—written in computer code to
sort through, organize, extract, or mine them has made inroads in almost every major social …

Advancements in computing and data from the near universal acceptance and
implementation of electronic health records has been formative for the growth of …

Artificial intelligence recommendations are sometimes erroneous and biased. In our
research, we hypothesized that people who perform a (simulated) medical diagnostic task …

Background Binary diagnosis of coronary artery disease does not preserve the complexity of
disease or quantify its severity or its associated risk with death; hence, a quantitative marker …

While there is significant enthusiasm in the medical community about the use of artificial
intelligence (AI) technologies in healthcare, few research studies have sought to assess …

Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …

The role of physicians has always been to synthesize the data available to them to identify
diagnostic patterns that guide treatment and follow response. Today, increasingly …

Major advances in diagnostic technologies are offering unprecedented insight into the
condition of the retina and beyond ocular disease. Digital images providing millions of …

Modern electronic health records (EHRs) provide data to answer clinically meaningful
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …

The digitalization of the health-care system has resulted in a deluge of clinical big data and
has prompted the rapid growth of data science in medicine. Data science, which is the field …