three-challenges-for-artificial-intelligence-in-medicine-dfb9993ae750#.5v4hyzqbl

There are three central challenges that have plagued past efforts to use artificial intelligence in medicine: the label problem, the deployment problem, and fear around regulation. With tools like Apple's ResearchKit and Google Fit, we can collect health data at scale; with deep learning, we can translate large volumes of raw data into insights that help both clinicians and patients take real actions. These annotations, called labels, are essential to make techniques like deep learning work. These two things enable outside-in approaches to healthcare: build up a user base outside the core of the healthcare system (e.g., outside the EMR), but take on risk for core problems within the healthcare system, such as re-hospitalizations.