Market Intel: Artificial Intelligence Brings Wave Of Future Health Care Innovation – Embrace it or be Left Behind
Artificial intelligence and machine-learning will be the biggest disrupters in the health care industry, forcing a major shift in how companies innovate and operate, offering physicians unprecedented tools to diagnose and treat patients to improve outcomes and connecting patients like never before through consumer-driven devices. This feature takes a close look at how smaller companies and giants like Abbott, as well as national health care systems, are leveraging the power of AI to expedite innovation and product development in diabetes and other diseases. It highlights some of the latest studies that show how AI is being harnessed to improve imaging analytics, predict hard-to-find risk factors for heart disease and detect cancer, and discusses the critical role tech giants such as IBM, Google and Amazon continue to play in this space.
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Results from a study of more than 91,000 ECG records collected with iRhythm’s Zio ambulatory continuous cardiac monitoring device that demonstrates the successful application of artificial intelligence to ECGs gives the 12-year-old company confidence that it will be profitable very soon.
Start-Up Spotlight: Payers, Pharma Clients Bank On Prognos' Artificial Intelligence To Predict Disease
New York-based health IT company Prognos recently secured $20.5m in Series C financing to build out its artificial intelligence capabilities, which it has applied to a giant diagnostic laboratory registry containing data from some 180 million patients. Given the shift to value-based health care and the high costs of treating chronic diseases, insurers might use information from this registry to predict the likelihood of disease and reach out to members to help head off illnesses. Pharma clients, in turn, can use the data to target physicians to help them deliver effective treatments.
Biopharmas and other life sciences firms risk getting left behind by technology firms if they don't begin addressing patient needs more directly through data-driven technological advances, such as those to aid clinical development or treatment decisions with real-time patient data.