Health IT funding set a record in 2017 with AI and predictive analytics as top tech funded, with patient engagement, telehealth and clinical decision support close behind.
Digital health firm HealthTap and Bupa, a health care provider that offers both insurance and medical services to millions around the world, are teaming up in a massive strategic partnership that could make “digital end-to-end” medical services a widespread reality, HealthTap CEO Ron Gutman stated in an early interview previewing the arrangement.
Artificial intelligence is gradually being adopted by health services to assist medics with the diagnosis of serious diseases. In one new development, scientists in Oxford, U.K. have launched an AI system for heart disease.
The ability to quantify the extent of kidney damage and predict the life remaining in the kidney, using an image obtained at the time when a patient visits the hospital for a kidney biopsy, now is possible using a computer model based on artificial intelligence (AI).
he gene-editing technology CRISPR could very well one day rid the world of its most devastating diseases, allowing us to simply edit away the genetic code responsible for an illness. One of the things standing in the way of turning that fantasy into reality, though, is the problem of off-target effects. Now Microsoft is hoping to use artificial intelligence to fix this problem.
The fear that machines will replace humans in the workplace is not a new one. In 1930, economist John Maynard Keynes conjectured that in the years to come, modern economies would face a new kind of affliction: what Keynes called “technological unemployment.”
Johnson & Johnson (J&J) has signed 15 new partnerships to explore, develop and advance new medical devices, therapeutics and consumer health solutions.
Microsoft has partnered with and invested in US-based Adaptive Biotechnologies to map the human immune system genetics using artificial intelligence (AI) for early detection and diagnosis of multiple diseases, including cancer.
The health system has developed AI-based algorithms used on its more than 27 petabytes of data to define patient subpopulations — those with congestive heart failure or asthma, for instance — to target interventions to those groups. It’s developed algorithms using electronic health record data to predict patient decline in hospitals.
The idea of personalised seizure prediction for epilepsy is closer to becoming a reality thanks to new research published today by the University of Melbourne and IBM Research-Australia.
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