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 aiding in the diagnosis of several diseases and health risks, and researchers have recently found that machine learning can help predict the onset of psychosis, a debilitating mental disorder that can compromise an individual’s psychology and their ability to think and feel.
Harvard researchers have used machine learning to predict which patients soon to undergo an operation for low-back pain should be monitored for opioid dependence down the road.
Combining AI with advances in acoustic engineering, Australian researchers have developed an algorithm that can help diagnose common pediatric respiratory conditions such as asthma, croup and pneumonia.
A year ago, U.S. military researchers presented an algorithm that can tell an individual how much caffeine to consume, and when, to achieve optimal alertness. Now they’ve turned the technique into a freely available tool for “designing effective strategies to maximize alertness while avoiding excessive caffeine consumption.”
Cancer patients undergoing chemotherapy often share word of their adverse reactions to the drugs in online health forums. Researchers at Stanford have used natural language processing to mine these posts, accurately flagging detrimental side effects well before clinical journals advise caution.
A convolutional neural network has proven better than traditional equations, indexes and scoring systems at predicting which hospitalized patients will face readmission not long after being sent home.
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.
