THANK YOU FOR SUBSCRIBING

As shown on the duvet page of the June 2017 issue of Newsweek, Silicon Valley thinks AI (AI) will cure our sick health care system. Really? As a CIO and MD scientist, I see hypes also as hopes. AI is becoming the new frontier in healthcare. And here’s why.

It is not the AI within the 1980s.

When I read my first AI textbook within the 1980s, I used to be amazed by the tremendous possibilities. As an example, by knowing the very fact that “Toby may be a dog” and “dogs have four legs”, the pc goes to infer that Toby has four legs. However, it's very hard to engineer all human knowledge into simple facts and rules. The theoretical great thing about first-order logic from the Prolog programing language couldn't solve the complexities within the world.

The AI in 2017, however, is far different. the main target has shifted from knowledge engineering to machine learning. Deep learning now plays a critical role to sift through an unprecedented amount of knowledge. As an example, self-driving cars are making decisions from one gigabyte of knowledge per second generated by dozens of sensors. This data-driven approach lifts the human burden on crafting choice rules for computers. From a machine learning perspective, the statistical worry of model overfitting is additionally alleviated by the exceedingly great deal of coaching data. 

The AI in 2017, however, is far different. It’s largely data-driven, where deep-learning plays a critical role

In healthcare, one place to start out is that the data-rich electronic health record system quite 90 percent of hospitals within the US are already using EHR systems to log demographics, diagnosis, treatment and payment information. There are enormous opportunities to show an EHR into a "smart" system, far beyond the present usage as a billing apparatus. 

For non-mission-critical applications, AI is prepared for clock time.

Besides IBM Watson Health, AI has powered numerous startups within the effort to rework healthcare. There has been a rush to take a position in AI companies.

Let’s be honest, AI is way from perfect. And healthcare is way more complex than a car drives it. The business opportunities of healthcare currently exist in non-mission-difficult applications. As an example, an AI system can assist nurse practitioners in sorting questions from patient portals faster; an assistive engine can run within the background to facilitate radiologists spotting the doubtless 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. Albeit AI applications roll bent the market quickly, physicians won't get replaced by machines anytime soon.

AI is often an excellent capital investment. 

The investment into AI also makes financial sense for healthcare organizations. Most hospitals are aware of large operating expenses on personnel. The investment into AI infrastructure, however, is taken into account a capital investment. Therefore, the value is depreciated throughout multiple years and therefore the return is evaluated with a long-term perspective.

In a word, as leaders of digital transformation, Healthcare CIOs need to attentively analyze the progress of AI and adopt an agile approach to align new technology with each healthcare organization’s business strategy.