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| January 30, 2019

The New AI: Why The FDA Is Not Enough

The New AI: Why The FDA Is Not Enough

The odds are good that radiologists want to believe in artificial intelligence (AI). The hype from vendors, professional societies and the media has been pointing them in that direction for the last couple years. Unfortunately, if history is a guide, there is a good chance that medical AI will fall short. This must not happen. The potential benefit of AI is too great for it to fail again.

The last time AI flopped was in the mid-1980s, after skyrocketing expectations. Sadly, failure was well within the mainstream of that period.

The medical community and public began the decade agog with antibodies made by patient cells hybridized with cancer, so-called “hybridomas.” These “magic bullets” were supposed to cure cancer. They did not.

“Cold fusion” ended much the same. A lot of sizzle. No steak.

There is a distinct possibility that we are setting ourselves up for the same kind of disappointment as we enter the third decade of the 21st century. Will the current foray into AI end in the same crater that befell the previous attempt? Or in the crater that became the resting place of the first “golden age” of AI, which thudded in the mid- to late-1940s?

 

Reason To Believe

As it has in these and other ill-considered endeavors, my profession is adding to this threat by stoking expectations about what AI might do. It’s easy to get caught up in the excitement — to herald the positives of AI and its “breakthroughs;” to present the opinions of AI advocates as fact when they are far from it.

While technically accurate in that the quoted and paraphrased statements about AI may indeed have been said by sources, the articles too often have been overly positive. The claims that AI might benefit the practice of medicine and patients are speculative. They are not sure things.

Acknowledging the role of magazines, newspapers and websites in hyping AI is less mea culpa than segue into the far weightier — and more critical — issue of how the medical community can keep from being disappointed. Doing so does not involve the development or validation of these algorithms —
but rather the careful evaluation of them.

As the leaders in medical imaging, upon which much AI effort has focused, radiologists must demand evidence that smart algorithms not only meet their claims but that they produce practical benefit.

 

What Regulators Do

You might think federal regulators (for example, those at the FDA) would be the ultimate arbiters of product claims. After all, they have been assigned to guard a government-constructed gate to the commercial market. Yet, as incongruous as it may seem, they typically review AI products through a process that compares them to commercial products. This is wrong for two reasons.

First, it is wrong-headed. This regulatory process, which results in a 510(k) clearance, requires that proposed products show “substantial equivalence” to ones already on the market. It is so-named because it refers to section 510(k) of the Federal Food, Drug and Cosmetic (FD&C) Act of 1938. The Medical Device Amendments of 1976 extended the FDA’s control to include medical devices.

By definition, AI products have no market precedents. They use algorithms that learn from data rather than ones that are programmed to perform specific tasks. As such, they are unique. (Although some vendors claim that their products are artificially intelligent even when they do not involve machine learning, for this commentary we will stick to machine learning as a necessary characteristic of AI.)

Second, since the 510(k) clearance process was enacted, the FDA has attempted — particularly in efforts in and around 1998 — to reduce the burden of a growing backlog of device applications. Today, the 510(k) process is a bureaucratic means for the FDA to expeditiously review applications for medical devices.

Consequently, the buyers of AI products, and the media who report on them, may be tempted to — but should not — believe that successfully completing FDA review attests to the value of sellers’ claims. This is unabashedly not the case. By not requiring clinically based evidence, the 510(k) process is typically chosen because it is the least intrusive of any regulatory mechanism and promises vendors the fastest and best return on their investments.

The FDA might accept them into this process because pushing applications through regulation blunts the charge often made by FDA critics, that the agency obstructs progress.

What no one — neither vendor nor regulator — says is that when AI products are reviewed through this process, the benefit of AI algorithms is seldom — if ever — part of the review process. This means the 510(k) clearance of a product for commercial sale is not enough reason for care providers to believe in it. Only the medical community can judge whether an AI product is beneficial.

 

Determining Value

Caveat emptor, therefore, is — and should be — in effect. The damages that come from making a wrong purchase decision could be to the care of the patient for whom the physician is directly responsible.

With so much at stake, it stands to reason that not only should the claims associated with an AI product be real, but the practical result of those claims should be validated or, at the very least, carefully examined. Further, claims and potential benefits should be vetted by providers before the product is applied. This goes for clinical and non-clinical algorithms alike, because even non-clinical algorithms designed for medical environments may impact patients.

For example, a vendor may claim that an AI algorithm can increase efficiency. A care provider might put such an algorithm into practice to reduce costs by increasing volume and throughput. In so doing, that algorithm might help staff accelerate their schedules. But failure to achieve this objective could make care less convenient for patients. The use of that algorithm, therefore, could impact patients.

Specifically, an algorithm might address patient positioning. Not only might its use affect the speed with which an exam is conducted and how well the staff stays on schedule, it might impact the amount of radiation the patient receives, thereby directly affecting patient safety.

While it may be obvious that AI must be held accountable, you might ask — on what criteria should providers evaluate claims? This gets back to the need for evidence to support claims.

While helpful, anecdotal evidence — stories that describe useful application of an algorithm — should not be considered sufficient. Statistically based evidence is needed to show incontrovertibly that the software lives up to claims — and that its use produces a practical benefit.

If, for example, improved positioning is the claim of an AI program, then the denominator of success should not be narrowly defined, for example, as a reduction in the number of adjustments made in patient positioning. Rather the practical benefit derived from implementing the algorithm should be at least one of the metrics. Is there evidence to indicate that use of the algorithm improves patient positioning so that it takes less time? If so, how might this allow the technologist either to accelerate the schedule or spend more time with the patient? Or — is there evidence that improved patient positioning due to the algorithm results in less patient exposure to radiation (and, if so, how much less)?

Yes, demanding evidence of practical benefit coming from AI sets the bar high. But that is where it needs to be, if AI is to avoid history’s painful lessons.

 

Related content:

Technology Report: Artificial Intelligence 2018

VIDEO: RSNA Post-game Report on Artificial Intelligence

VIDEO: AI, Analytics and Informatics: The Future is Here

 

Related Content

Samsung Demonstrates Viability of Lower Dose Digital Radiography Algorithm for Pediatric Patients
News | Digital Radiography (DR) | April 24, 2019
Samsung announced that its new image post-processing engine (IPE), S-Vue 3.02, recently received U.S. Food and Drug...
Konica Minolta KDR AU and KDR Primary DR Systems Receive Seismic Certification
News | Digital Radiography (DR) | April 24, 2019
Konica Minolta Healthcare Americas Inc., announced its KDR Advanced U-Arm and KDR Primary Digital Radiography System...
Cianna Medical featured its wire-free marker system on the exhibit floor of the breast imaging symposium in Hollywood, Fla.

Cianna Medical featured its wire-free marker system on the exhibit floor of the breast imaging symposium in Hollywood, Fla.

Feature | Breast Imaging | April 24, 2019 | By Greg Freiherr
Wires have traditionally been placed prior to lumpectomy to mark cancerous tissues in the breast.
Konica Minolta Dynamic Digital Radiography Receives FDA Clearance

With DDR, orthopedists and MSK specialists can acquire a full view of the MSK system in the supine and prone positions to view changes in the bone and articulations throughout the full range of motion. Image courtesy of Konica Minolta Healthcare Americas.

Technology | Digital Radiography (DR) | April 23, 2019
Konica Minolta Healthcare Americas Inc. announced that its Dynamic Digital Radiography (DDR) technology, introduced at...
Graphic courtesy of Pixabay

Graphic courtesy of Pixabay

Feature | Artificial Intelligence | April 22, 2019 | By Greg Freiherr
...
HHS Extends Comment Period for Proposed Electronic Health Information Interoperability Rules
News | Electronic Medical Records (EMR) | April 19, 2019
The U.S. Department of Health and Human Services (HHS) is extending the public comment period by 30 days for two...
FDA Clears GE's Deep Learning Image Reconstruction Engine
Technology | Computed Tomography (CT) | April 19, 2019
GE Healthcare has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) of its Deep Learning Image...
In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images. Photo by Greg Freiherr

Feature | Artificial Intelligence | April 19, 2019 | By Greg Freiherr
Commercial efforts to develop...
Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
Atrium Health Debuts Amazon Alexa Skill to Help Patients Access Medical Care
News | Artificial Intelligence | April 18, 2019
Atrium Health patients will now be able to use Amazon’s electronic voice system Alexa to not only locate the nearest...