Artificial Intelligence In Healthcare: Separating Reality From Hype
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
In healthcare today, the most commonly used “AI” applications are algorithmic: evidence-based approaches programmed by researchers and clinicians.
When humans embed known data into algorithms, computers can extract information and apply it to a problem. Take cancer treatment, for example. Using consensus algorithms from experts in the field, along with the data that oncologists enter into a medical record (i.e., a patient’s age, genetics, cancer staging and associated medical problems), a computer can review dozens, sometimes hundreds, of established treatment alternatives and recommend the most appropriate combination of chemotherapy drugs for a patient.
Perhaps my favorite algorithmic solution comes by way of Dr. Gabriel Escobar and his colleagues in The Permanente Medical Group’s division of research.
The team’s research centered on one of the most important populations in any hospital: patients in a medical or surgical unit who will experience a deterioration in clinical status and be transferred to the ICU.
Though these patients receive intensive care for an acute event, and seemingly return to their prior health status, they are three to four times more likely to die than if a physician had intervened and prevented the deterioration in the first place.
Dr. Escobar, along with division chief Dr. Tracy Lieu and associate executive director Dr. Philip Madvig, compiled data from 650,000 hospitalized patients, 20,000 of whom required this type of ICU transfer.
The team then created a predictive analytic model to identify which hospitalized patients today are most likely to end up in the ICU tomorrow. They then embedded the algorithm into a computer system, which continuously monitors the health status of all hospitalized patients. Finally, they designed alerts to notify physicians whenever a patient is deemed “at risk.” With this information, the doctors can intervene in advance of a major complication and save hundreds more lives each year.