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.
Machine learning (ML) can provide significant value in the field of palliative care. However, researchers still have a lot of unexplored ground to cover before the technology reaches its full potential.
Chun Yuan, PhD, has received a two-year, $200,000 grant from the American Heart Association’s Institute for Precision Cardiovascular Medicine for his work on using AI to detect blocked arteries and cardiovascular risk.
Researchers have developed a multitask deep learning model that can effectively assess signs of hip osteoarthritis in x-rays, sharing their findings in Radiology.
The rise of AI in healthcare—especially radiology—has launched countless conversations about ethics, bias and the difference between “right” and “wrong.”
Radiology researchers are turning to deep learning (DL) technology to make NLP even more effective—and it’s a growing trend that shows no signs of slowing down.
AI can help improve malaria screening in low-resource settings, according to a new study published in the Journal of Digital Imaging. The model developed by researchers is as precise as human experts—and “several orders of magnitude” faster.
