Why AI is the new frontier in healthcare?
By Simon Lin, MD, MBA, Research CIO, Nationwide Children’s Hospital
It is no longer the AI in the 1980s.
When I read my first AI text book in the 1980s, I was amazed by the tremendous possibilities. For instance, by knowing the fact that “Toby is a dog” and “dogs have four legs”, the computer is going to infer that Toby has four legs. However, it is very hard to engineer all human knowledge into simple facts and rules. The theoretical beauty of first-order logic from the Prolog programming language could not solve the complexities in the real world.
The AI today, however, is much different. The focus has shifted from knowledge engineering to machine learning. Deep learning now plays a critical role to sift through extraordinary amount of data. For instance, self-driving cars are making decisions from gigabytes of data per second generated by dozens of sensors. This data-driven approach lifts the human burden on crafting the decision rules for computers. From a machine learning perspective, the statistical worry of model over fitting is also alleviated by the exceedingly large amount of training data.
The AI today, however, is much different. It is largely data-driven, where deep-learning plays a critical role
In healthcare, one place to start is the data-rich electronic health record system (EHR). More than 90 percent of hospitals in the U.S. are already using EHR systems to log demographic, diagnosis, treatment and payment information. There are enormous opportunities to turn an EHR into a "smart" system, far beyond the current usage as a billing apparatus.
For non-mission-critical applications, AI is ready for prime time.
Besides IBM Watson Health, AI has powered numerous startups in the effort to transform healthcare. There has been a rush to invest into AI companies.
Let’s be honest, AI is far from perfect. And healthcare is far more complex than a car drives itself. The business opportunities of healthcare currently exist in the non-mission-critical applications. For instance, an AI system can assist nurse practitioners in sorting questions from patient portals faster; an assistive engine can run in the background to facilitate radiologists spotting the potentially overlooked abnormalities; a cognitive computing platform can improve patient satisfaction of a 24-hour nurse line by analyzing the patient sentiment in real time.
That being said, humans are still the "golden standard" of intelligence for mission-critical applications. Even though AI applications roll out to the market quickly, physicians will not be replaced by machines anytime soon.
AI can be a great capital investment.
The investment into AI also makes financial sense for healthcare organizations. Most hospitals are conscious on large operating expense on personnel. The investment into AI infrastructure, however, is considered capital investment. Therefore, the cost is depreciated throughout multiple years and the return is evaluated with a long-term perspective.
In a word, as leaders of digital transformation, Healthcare CIOs ought to carefully monitor the progress of AI and adopt an agile approach to align new technology with each healthcare organization’s business strategy.