In-Depth: AI in Healthcare
The days of claiming artificial intelligence as a feature that set one startup or company apart from the others are over. These days, one would be hard-pressed to find any technology company attracting venture funding or partnerships that doesn’t posit to use some form of machine learning. But for companies trying to innovate in healthcare using artificial intelligence, the stakes are considerably higher, meaning the hype surrounding the buzzword can be deflated far more quickly than in some other industry, where a mistaken algorithm doesn’t mean the difference between life and death.
Over the past five years, the number of digital health companies employing some form of artificial intelligence has dramatically increased. CB Insightstracked 100 AI-focused healthcare companies just this year, and noted 50 had raised their first equity rounds since January 2015. Deals in the space grew from under 20 in 2012 to nearly 70 in 2016. A recent survey found that more than half of hospitals plan to adopt artificial intelligence within 5 years, and 35 percent plan to do so within two years. In Boston, Partners HealthCare just announced a 10-year collaboration with GE Healthcare to integrate deep learning technology across their network. The applications for AI go far beyond just improving clinician workflow and processing claims faster.
“The problem we are trying to solve is one of productivity,” Andy Slavitt, the former acting administrator of the Centers for Medicare and Medicaid Services said during the Light Forum, a two-day conference that brought together CEOs, healthcare IT experts, policymakers and physicians at Stanford University last week. “We need to be taking care of more people with less resources, but if we chase too many problems and business models or try to invent new gadgets, that’s not going to change productivity. That’s where data and machine learning capabilities will come in.”
Respondents to the hospital survey said the technology could have the most impact on population health, clinical decision support, diagnostic tools and precision medicine. Even drug development, real world evidence collection and clinical trials could be faster, cheaper and more accurate with AI. But the time to put all of our faith in AI is still not here. “The human brain is a really strong prior on what makes sense,” Andrew Maas, chief scientist and cofounder of Roam Analytics said during the Light Forum. “Computers are powerful on assessing, but not on the level of reliability you will trust soon.”
How do we get there?
So everybody wants it, but just how soon will we see the purported transformation of healthcare from machine learning? Lately, we’ve seen it in everything from the most straightforward app to the most complex diagnostic tasks, coming in the form of natural language processing or image recognition to powerful algorithms crunching databases made up of decades of medical research.
Like any other technology in healthcare, AI can’t be brought in without a mountain of extra challenges including regulatory barriers, interoperability with legacy hospital IT systems, and serious limitations on access to crucial medical data needed to build powerful health-focused algorithms in the first place. But that’s not stopping innovation, albeit cautious innovation, and digital health stakeholders are realizing that unlocking AI’s true full potential requires strategic partnerships, quality data, and a sober understanding of statistics.
As the understanding of AI in healthcare matures, the biggest names in technology aren’t shying away from the mountainous challenges that come with innovating in the industry, like regulatory barriers, legal access to quality data and the constant issue of lack of interoperability. Just this week,Google announced it has built upon its tried and true consumer-level machine learning capabilities into healthcare.
