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.
AI is poised to help settle an argument that’s been roiling academic psychiatry for more than a century: Are bipolar disorder and schizophrenia two distinct diagnoses—or points along a single continuum?
A novel AI-based model for clinical decision support has bested established machine-learning models at predicting how patients with type-2 diabetes mellitus will respond to various categories of therapeutic drugs.
Researchers have developed an AI system that can differentiate primary tumors from metastatic lookalikes on routine histology slides while also helping pinpoint the sites from which the cancer sprung.
Yale researchers have demonstrated a machine learning tool for choosing between coronary imaging and stress testing in patients who present with suspected coronary artery disease.
Biases in algorithms can have critical implications for minority patients, which is why IBM Research and Watson Health sought out to examine the best methods for addressing this problem.
Researchers have used machine learning to predict wellbeing—not only objective physical condition but also subjective overall health—as a function of demographic, socioeconomic and geographic factors.
Johns Hopkins researchers have used deep neural networks to draw important insights—prescriptive as well as descriptive—into adaptive immunity from massive stores of T-cell receptor sequencing data.
U.S. health systems are increasingly leveraging digital health to conduct their operations, but how health systems are using digital health in their strategies can vary widely.
When human counselors are unavailable to provide work-based wellness coaching, robots can substitute—as long as the workers are comfortable with emerging technologies and the machines aren’t overly humanlike.
A vendor that supplies EHR software to public health agencies is partnering with a health-tech startup in the cloud-communications space to equip state and local governments for managing their response to the COVID-19 crisis.
