As shown on the cover page of the June 2017 issue of Newsweek, the Silicon Valley thinks Artificial Intelligence (AI) will cure our sick health care system. Really? As a CIO and MD scientist, I see hypes as well as hopes. AI is becoming the new frontier in healthcare. And here’s why.
It is no longer the AI in the 1980s.
When I read my first AI textbook 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 in 2017, however, is much different. The focus has shifted from knowledge engineering to machine learning. Deep learning now plays a critical role to sift through an extraordinary amount of data. For instance, self-driving cars are making decisions from one gigabyte 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 overfitting is also alleviated by the exceedingly large amount of training data.
"The AI in 2017, 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 US 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 the 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 of large operating expense on personnel. The investment into AI infrastructure, however, is considered a 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