Cardiogram tested the deep neural network it had built against 51 in-hospital cardioversions (a procedure that restores the heart’s normal rhythm) and says it achieved a 97 percent accuracy in the neural network’s ability to find irregular heart activity.
So far this is just a study built on a preliminary algorithm but it holds promise in trying to identify and prevent stroke in the future.
AtomNet is the first software of its kind, and it may soon see others following in its footsteps in the effort to apply AI to large-scale challenges.
AI and machine learning are forcing dramatic business model change for all the stakeholders in the health care system.
Although it might not be practical (yet) to order life insurance through Alexa, the idea of a fully machine-powered process for financially protecting your loved ones may not be that far away.
The combination of data and AI was not available until the past year or two, and it can lead to the kind of automation that has disrupted so many other industries. Many health care entrepreneurs are focused precisely on the win-win-win prospect of lowering the cost of care while making it better and available to more people.
The Machines Are Getting Ready to Play Doctor
Emerging applications of artificial intelligence (AI), as well as medical research trends, suggests that we are moving toward fulfilling medicine’s aim and achieving its ideal.
AI systems are proving to be better than human healthcare professionals at everything from predicting the development of mental illness to recommending cancer treatment plans.
Although the use of AI in healthcare is still relatively new, it won’t be long before we start reaping the full benefits of its potential.
While much of the conversation around AI and jobs is focused on widespread job losses in sectors like trucking, venture capitalist and Sun Microsystems cofounder Vinod Khosla thinks that there’s a high-paying job on the chopping block: oncology.
His comments were part of a broader point that education will not be enough to stem the economic upheaval and job loss that comes about as part of the growth of artificial intelligence. He doesn’t believe that human radiologists will exist in five years as a result, for example.
AI And Diagnosis
It’s possible that the next time you go in for something that stumps your regular physician, instead of seeing a specialist across town, you’ll see five or 10 from around the country. All it takes is a few minutes over lunch or in an elevator to put on a Sherlock Holmes hat, hop into the cloud, and sleuth through your case.
What happens when diagnosis is automated?
A pair of recently developed AI systems can diagnose lung cancer and heart disease more accurately than human doctors. These AIs have the potential to save billions of dollars and countless lives if widely adopted.
AI in medicine is changing healthcare as we know it. The introduction of deep learning systems is only possible by powerful computing capabilities; capabilities that Nvidia has made possible with their graphic processors.
Doctors have developed a deep learning algorithm that can cut the time needed to analyze and classify a tissue sample during surgery from 30 to 40 minutes to just 3 or 4.
In hopes of creating better access to medical care, Stanford researchers have trained an algorithm to diagnose skin cancer.
US scientists are using artificial intelligence to predict whether breast lesions identified from a biopsy will turn out to cancerous.