Also called personalized medicine, this evolving field makes use of an individual’s genes, lifestyle, environment and other factors to identify unique disease risks and guide treatment decision-making.
Cynthia Rudin, PhD, is a highly regarded computer scientist who’s been eyeing the advance of artificial intelligence into society with equal parts enthusiasm and concern.
By now it’s a difficult-to-dispute likelihood: AI won’t replace doctors making diagnoses, but doctors who use AI will displace doctors who don’t use AI. The hypothesis gets a fresh airing out from the vantage point of the general public.
RSNA has announced that the official registration number for RSNA 2019 was 51,800, with another 6,754 participants registering for the online virtual meeting.
Patients can now use AI to monitor their glucose levels with off-the-shelf, noninvasive wearable sensors, according to a new study published in Scientific Reports.
Radiomics and machine learning can help healthcare providers determine if late gadolinium enhancement (LGE) on cardiac MR images is a sign of myocardial infarction (MI) or myocarditis.
Deep learning could potentially assist healthcare providers with the evaluation of small renal masses detected on certain contrast-enhanced CT exams, according to a new study published in the American Journal of Roentgenology.
Machine learning (ML) technology has gained popularity in recent years, but its use in healthcare remains largely limited to proof-of-concept academic studies, according to a new study published in Artificial Intelligence in Medicine.
Researchers have developed a deep learning system capable of evaluating tissue samples and diagnosing prostate cancer at a level comparable with many pathologists.
