Greg Freiherr, Industry Consultant
Greg Freiherr, Industry Consultant

Greg Freiherr has reported on developments in radiology since 1983. He runs the consulting service, The Freiherr Group.

Blog | Greg Freiherr, Industry Consultant | Artificial Intelligence| September 07, 2018

AI and Innovation: When Intelligence is No Longer “Artificial”

Like the industrial revolution, which led to wrenching changes in society (for example, factory robotics and automatic pinsetters at the ends of bowling alleys), the widening use of artificial intelligence (AI) will change the American workforce. We are only seeing the ripples of what may turn into the wake of major innovation.

The last time something like this happened in radiology was 40 years ago with positron emission tomography (PET) and magnetic resonance imaging (MRI). Since then we have adopted small innovations and pretended they were big. Artificial intelligence could force radiology to break with that.

But what will we call this? AI might not be the right term. Machine learning is better. And it’s more than semantics.

 

What’s In A Word

The meaning of words change over time. Remember when “dialing” meant calling on the phone? Remember when the phone wasn’t called a “landline?” Remember when the phone was for talking?

A GPS app on my smartphone tells me how to get to from one place to another. Double-clicking its side button brings up a high-res camera. I type notes into a word processor; record reminders to myself on a digital recorder app. I’ve stopped wearing a watch. (The time display on my phone covers a third of the display.)

The next logical step is a phone that learns.

Wise people credit their success to surrounding themselves with smart people. Someday I’d like to say the same about my machines.

Classifying “AI” as machine learning will help buoy the argument that intelligent machines are assistants, not replacements.

 

Flies In The Soup

But there’s a problem. It has to do with manufacturers making money. Machines that learn may not become obsolete very easily. And planned obsolescence is important. Take the light bulb, for example.

Demonstrating the folly of a long-lasting light bulb is the one made more than a century ago by the Shelby Electric Co. of Ohio. It’s been turned off only a handful of times. Yet that bulb is now in its 117th year of illumination. The town of Livermore, Calif., celebrated the bulb’s 1 million hours of operation in 2015, according to the Centennial Bulb website.

If all light bulbs were built to last a century or longer, comparatively few would have been sold. And that is the underlying problem with AI (aka machine learning).

How do you update machines that learn? Improved processors? Maybe. Better learning ability? Perhaps. But if I had a machine that constantly got better at doing what I needed it to do, why would I trade it in or even update it?

The makers of learning machines will solve this problem. A less surmountable barrier, however, is difficulty building machines that will actually add value to medicine. To do that, people have to get involved. And there is the real problem. Physicians and patients will have to be convinced that learning machines are worth the risk; that they can be built and used without risking the future of humankind. Some of that persuasion is already in the works.

Siri, Alexa and Cortana are reshaping the ways people interact with computers and, in the process, how we think about computers. Further changing our views of computers are virtual and augmented realities, which deliver information when and where needed. Whether the public will ultimately embrace machine learning as it relates to medical practice, however, is anything but certain.

Look no further than GMOs (genetically manipulated organisms) for an example of how something with enormous potential can flounder. The controversy swirling around GMOs has impeded the acceptance of what decades ago was supposed to bring an unprecedented abundance of food. The core concern of so-called Frankenfoods — their safety — continues to be debated. As stated in a New York Times story in April 2018, some consumers seem “terrified of eating an apple with an added anti-browning gene or a pink pineapple genetically enriched with the antioxidant lycopene.”

It is sobering to note that GMO fears are still theoretical. And yet, they have been stopped in their tracks. The bottom line is that GMO foods can never be proven safe. They can only be shown to present no hazard, as of yet. Ditto for AI.

 

Making IntelligentMachines Palatable

The adoption of machine learning in medicine will only occur with “baby steps.” A crucial one is making these machines palatable to mainstream radiologists.

To do so, safeguards must be put in place to ensure that learning machines are designed only to help. Doing so will go a long way toward alleviating the fear surrounding AI today.

The second crucial “baby step” involves demonstrating value. Learning machines must deliver on the promise of value-based medicine. They must help improve patient care (possibly measured by patient outcomes), and boost efficiency and cost effectiveness.

The third step that needs taking: Learning machines have to be shown to promote patient engagement in healthcare. Maybe this will happen by helping patients live healthier. Or maybe by providing more time for physicians to spend with their patients, taking on time consuming burdens or helping in communicating difficult concepts. There are lots of possibilities.

The takeaway is that learning machines have to demonstrate value in the humdrum metrics that now characterize the practice of medicine. And they have to be usable.

I can imagine a time when learning machines are distributed across multiple devices — tablets and desktops, smartphones and TVs, maybe even dedicated boxes like Amazon Echoes. Each will use a voice interface to promote efficiency with providers and patients. And, as they learn what we need, we get more efficient. And that has to be provable.

This future will happen only with the coming together of different but complementary technologies, along with a public recognition that these technologies are making a positive difference. Development has to be done cautiously and safely, with benefits proven and documented along the way. Otherwise fear will win out.

And AI will go the way of another acronym now associated more with Frankenstein than progress.

Related Content

Varian Unveils Ethos Solution for Adaptive Radiation Therapy
News | Image Guided Radiation Therapy (IGRT) | September 16, 2019
At the 2019 American Society for Radiation Oncology (ASTRO) annual meeting, being held Sept. 15-18 in Chicago, Varian...
FDA Clears GE Healthcare's Critical Care Suite Chest X-ray AI
Technology | X-Ray | September 12, 2019
GE Healthcare announced the U.S. Food and Drug Administration’s (FDA) 510(k) clearance of Critical Care Suite, a...
iCAD's ProFound AI Wins Best New Radiology Solution in 2019 MedTech Breakthrough Awards
News | Computer-Aided Detection Software | September 09, 2019
iCAD Inc. announced MedTech Breakthrough, an independent organization that recognizes the top companies and solutions...
Imaging Biometrics and Medical College of Wisconsin Awarded NIH Grant
News | Neuro Imaging | September 09, 2019
Imaging Biometrics LLC (IB), in collaboration with the Medical College of Wisconsin (MCW), has received a $2.75 million...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used deep learning, a form of machine learning, which is a type of artificial intelligence. Image courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Society of Breast Imaging (SBI) | September 06, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Philips and Fujifilm booths at SIIM 2019.

Philips and Fujifilm booths at SIIM 2019.

Feature | SIIM | September 06, 2019 | By Greg Freiherr
Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Inform
Sudhen Desai, M.D.

Sudhen Desai, M.D.

Feature | Pediatric Imaging | September 04, 2019 | By Jeff Zagoudis
Burnout has become a popular buzzword in today’s business world, meant to describe prolonged periods of stress in the
Heath information technology diagram showing use of cloud storage.
Feature | Archive Cloud Storage | September 04, 2019 | Tyna Callahan
In healthcare, critical systems are being used to deliver vital information and services 24x7x365.
Global Diagnostics Australia Incorporates AI Into Radiology Applications
News | Artificial Intelligence | September 04, 2019
Global Diagnostics Australia (GDA), a subsidiary of the Integral Diagnostics Group (IDX), has adopted artificial...
The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

Sponsored Content | Case Study | Radiation Dose Management | September 04, 2019
Radiation dose management is central to child patient safety. Medical imaging plays an increasing role in the accurate...