When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on healthcare. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. we could achieve exponential breakthroughs. Deep learning first caught the media's attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford's School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists. The ultimate dream in healthcare is to eradicate disease entirely.
Get ready for the next wave of predictive analytics, capable of identifying future admissions and health plan disenrollments. Until recently, many of the machine learning applications talked about for healthcare had been used to teach computing systems enough to be able to suggest a diagnosis on a specific disease. It essentially sent Watson to medical school. IBM had Watson ingest large amounts of medical literature to learn everything physicians are taught about patients' conditions, and then taught it to make diagnoses. But a Harvard professor who leads a startup supplying machine learning technology to Senior Whole Health, a Medicaid managed care organization active in New York state and Massachusetts, says that machine learning will eventually power all technologies we know today as predictive analytics and population health.
The hype surrounding so-called Big Data – the computational analysis of vast data sets to uncover patterns, trends and associations – is "bi-polar." That's how Leonard D'Avolio, an assistant professor at Harvard Medical School, describes all the chatter around this technology. "Either we are reading about how Big Data will cure cancer or about how it's foolish to believe Big Data will replace doctors," D'Avolio said. "I think the narrative should be in the middle, where we are talking about these technologies as tools that could be used to complement the work of not just clinicians but also healthcare administrators, operational leaders and others. Big Data is another set of technologies with pros and cons."