3 Ways AI Is Changing Healthcare

THE CURRENT U.S. HEALTH CARE PICTURE is pretty bleak: more than 12 million serious diagnostic errors each year, a third of the $3.6 trillion spent attributed to waste, reduction in life expectancy for what will be three years in a row (which is unpre cedented), and peak levels of physician burnout, depression, and suicide. That's all happening at a time when there is more medical data per individual than ever, imagined with wearable sensor physiology, scan anatomy (above), DNA sequencing, gut microbiome biology, just to name a few layers. Enter deep-learning A.I., with neural networks that will impact every type of clinician, from helping to accurately read scans, slides, skin lesions, eyegrounds, and more, to health systems, promoting the use of remote monitoring that ultimately obviates the need for regular hospital rooms, and at the consumer level, by providing a virtual medical coach to better manage or even prevent diseases. But it's our best shot to deal with all of the formidable challenges: to use the wealth of data to reduce errors and waste, and the gift of time to markedly improve the clinician-patient relationship. IN JUST THE PAST few years, there have emerged credible if still-in-the-works A.I.-powered technologies that can read radiology scans (like Imagen), identify tumors and track the spread of cancer (Arterys), detect eye conditions using retinal imaging (Google's DeepMind), flag dangerously abnormal potassium levels via a "bloodless blood test" (Mayo Clinic Ventures and AliveCor), and otherwise assist with the tricky business of diagnosing, or even predicting, disease.