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 should not be used to predict the course of clinical depression if all it has to work with are new patients’ medical records of past diagnoses, medications, encounters and patient-reported outcomes.
AI and machine learning are adept at selecting good candidates for total joint arthroplasty and predicting which patients will and won’t experience post-op complications.
Researchers in the U.S. and China have meshed AI with blood testing and CT lung imaging to accurately predict which newly diagnosed COVID-19 patients will need a mechanical ventilator.
AI has shown strong potential for predicting which recently hospitalized patients will develop pressure injuries (PIs), also known as pressure ulcers or bedsores, if they aren’t treated early with preventive medicine.
After training deep neural networks on around 4,000 slide images from around 40 biopsied kidney patients, UCLA engineers have virtually re-stained tissue images for speedier high-accuracy diagnostics than a human histotechnologist could support.
Researchers have achieved accuracies of 99.4% and 94.3% in two algorithmic methods for monitoring, diagnosing or ruling out Parkinson’s disease going only by individuals’ spoken words.
Screening for sepsis in children and babies has grown quickly over the past several years. As methods and approaches multiply, machine learning continues looking like an eventual first-line diagnostic option.
Researchers have used machine learning to accurately predict when a patient with chronic kidney disease will need dialysis. The technique may facilitate personalized care and optimized treatment planning.
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.
