Artificial Intelligence In Healthcare: Separating Reality From Hype Posted on : Mar 13 - 2018

It’s impossible to read about the future of healthcare without encountering two pixilated vowels that, together, represent the hopes and fears of an industry seeking more intelligent solutions.

Though the field of artificial intelligence (AI) has been around since 1956, it has made precious few contributions to medical practice. Only recently has the hype of machine-based learning begun to merge with reality.

What Is Artificial Intelligence, Really?

Confusion surrounding AI – its applications in healthcare and even its definition – remains widespread in popular media. Today, AI is shorthand for any task a computer can perform just as well as, if not better than, humans.

But there are different forms of computer intelligence to consider when thinking about its role in medicine.

Most of the computer-generated solutions now emerging in healthcare do not rely on independent computer intelligence. Rather, they use human-created algorithms as the basis for analyzing data and recommending treatments.

By contrast, “machine learning” relies on neural networks (a computer system modeled on the human brain). Such applications involve multilevel probabilistic analysis, allowing computers to simulate and even expand on the way the human mind processes data. As a result, not even the programmers can be sure how their computer programs will derive solutions.

There’s yet another AI variant, known as “deep learning,” wherein software learns to recognize patterns in distinct layers. In healthcare, this mechanism is becoming increasingly useful. Because each neural-network layer operates both independently and in concert – separating aspects such as color, size and shape before integrating the outcomes – these newer visual tools hold the promise of transforming diagnostic medicine and can even search for cancer at the individual cell level.

AI can be sliced and diced many different ways, but the best way to understand its potential use in healthcare is to break down its applications into three separate categories: algorithmic solutions, visual tools and medical practice. View More