Supercharge healthcare with artificial intelligence Posted on : Apr 22 - 2017

Artificial intelligence is redesigning healthcare. How do CIOs get a grip on what’s practical versus theoretical in the dynamic space of machine intelligence? It starts by framing the A.I. landscape.

Pattern-recognition algorithms can transform horses into zebras; winter scenes can become summer; artificial intelligence algorithms can generate art; robot radiologists can analyze your X-rays with remarkable precision.

We have reached the point where pattern-recognition algorithms and artificial intelligence (A.I.) are more accurate than humans at the visual diagnosis and observation of X-rays, stained breast cancer slides and other medical signs involving general correlations between normal and abnormal health patterns.

Before we run off and fire all the doctors, let’s better understand the A.I. landscape and the technology's broad capabilities. A.I. won’t replace doctors — it will help to empower them and extend their reach, improving patient outcomes.

An evolution of machine learning

The challenge with artificial intelligence is that no single and agreed-upon definition exists. Nils Nilsson defined A.I. as “activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.” But that definition isn't close to describing how A.I. evolved.

Artificial intelligence began with the Turing Test, proposed in 1950 by Alan Turing, the scientist, cryptanalyst and theoretical biologist. Since then, rapid progress has been made over the last 75 years, advancing A.I. capabilities.

Isaac Asimov proposed the Three Laws of Robotics in 1950. The first A.I. program was coded in 1951. In 1959, MIT began research in the field of artificial intelligence. GM introduced the first robot into its production assembly line in 1961. The 1960s were transformative, with the first machine learning program written and the first demonstration of an A.I. program which understood natural language, and the first chatbot emerged. In the 1970s, the first autonomous vehicle was designed at the Stanford A.I. lab. Healthcare applications for A.I. were first introduced in 1974, along with an expert system for medical diagnostics. The LISP language emerged out of the 1980s, with natural networks integrating with autonomous vehicles. IBM’s famous Deep Blue beat Gary Kasparov at chess in 1997. And by 1999, the world was experimenting with A.I.-based “domesticated” robots.

Innovation was further inspired in 2004 when DARPA hosted the first design competition for autonomous vehicles in the commercial sector. By 2005, big tech companies, including IBM, Microsoft, Google and Facebook, were actively investing in commercial applications, and the first recommendation engines surfaced. The highlight of 2009 was Google’s first self-driving car, some three decades after the first autonomous vehicle was tested at Stanford. View More