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.
People who struggle to get a good night’s sleep and seek medical help for the problem are producing mega data on things like eye movement, breathing, brain activity and restless legs. Which is to say sleep medicine is as ripe as any field in healthcare for help from AI.
A Colorado teenager has won $75,000 for his machine learning and computer vision project that helps orthopedic surgeons improve the accuracy of screw placement during spinal surgery.
Contrary to common understanding, sepsis is not a one-size-afflicts-all condition. It’s a set of many, and each can be placed into one of four key subcategories—i.e., phenotypes—such that, going forward, stricken patients could receive tailored treatments.
A natural language processing algorithm has achieved 90% precision in automatically spotting signs of social isolation in cancer patients by “reading” clinical notes in a hospital’s electronic health record.
Academic and popular writings on the use of “embodied” AI in mental healthcare are piling up fast. But where’s the guidance for psychiatrists, psychotherapists and clinical social workers looking to use robots, avatars and chatbots with real patients?
Instead of Googling symptoms to when feeling an ailment and landing on an incorrect diagnosis, AI could soon provide accurate diagnosis without needing to go to a doctor’s office.
AI can speed up precise detection of one of the key signs of Alzheimer’s disease, according to researchers from University of California Davis and UC San Francisco, who published a study on their machine learning tool in Nature Communications.
Radiology, the medical specialty into which AI has made the furthest initial inroads in the U.S., is embracing the technology in France. And this is so despite French radiologists feeling underinformed on AI up to now.
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.
