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.
Masimo's MightySat Medical is the first FDA-cleared pulse oximeter available to consumers without a prescription, which could disrupt the market for the notoriously inaccurate at-home devices.
MediView’s technologies utilize AR to provide clinicians with 3D “X-ray vision” guidance during minimally invasive procedures and surgeries, while also offering remote collaboration.
Eye exams that leverage machine learning to track eye movement behavior are providing universities, research institutions and governmental agencies with more than a billion data points to better understand the connection between vision and neurological disease, according to health tech company RightEye.
Not-for-profit network Sutter Health and AI company Suki are partnering to deliver an AI-powered, voice-enabled digital assistant for doctors in Northern California.
Fourteen startups are getting a share of $117 million from Intel Capital, which is encouraging the recipients to spend the money on efforts to “disrupt their industries.” For two of the companies, the industry is healthcare and the disruptive innovation involves AI.
Combing social networks and pharmaceutical databases using heterogeneous network mining allowed a team from Drexel University to better identify therapies suitable for drug repositioning, according to work published in Artificial Intelligence in Medicine.
It’s been 33 years since the federal government began requiring hospitals to take in patients after stabilization in the ED if the case called for admission and the hospital had the clinical expertise to provide the extended care. Yet Medicaid patients and the uninsured still have higher odds of getting transferred than those with high-quality insurance plans.
A free web tool known as “Chester the AI Radiology Assistant” can assess a person’s chest X-rays online within seconds, ensuring patients’ private medical data remains secure while predicting their likelihood of having 14 diseases.
Machine learning approaches including deep learning and random forest greatly improved a University of Nottingham team’s ability to predict premature death in a study of half a million U.K. Biobank participants, according to research published in PLOS One.
