ImagingAI Archives - The Imaging Wire

Economic Barriers to AI

Posted on August 27, 2023August 26, 2023 by Brian Casey

A new article in JACR highlights the economic barriers that are limiting wider adoption of AI in healthcare in the US. The study paints a picture of how the complex nature of Medicare reimbursement puts the country at risk of falling behind other nations in the quest to implement healthcare AI on a national scale.

The success of any new medical technology in the US has always been linked to whether physicians can get reimbursed for using it. But there are a variety of paths to reimbursement in the Medicare system, each one with its own rules and idiosyncrasies.

The establishment of the NTAP program was thought to be a milestone in paying for AI for inpatients, for example, but the JACR authors note that NTAP payments are time-limited for no more than three years. A variety of other factors are limiting AI reimbursement, including …

All of the AI payments approved under the NTAP program have expired, and as such no AI algorithm is being reimbursed under NTAP
Budget-neutral requirements in the Medicare Physician Fee Schedule mean that AI reimbursement is often a zero-sum game. Payments made for one service (such as AI) must be offset by reductions for something else
Only one imaging AI algorithm has successfully navigated CMS to achieve Category I reimbursement in the Physician Fee Schedule, starting in 2024 for fractional flow reserve (FFR) analysis

Standing in stark contrast to the Medicare system is the NHS in the UK, where regulators see AI as an invaluable tool to address chronic workforce shortages in radiology and are taking aggressive action to promote its adoption. Not only has NHS announced a £21M fund to fuel AI adoption, but it is mulling the implementation of a national platform to enable AI algorithms to be accessed within standard radiology workflow.

The Takeaway

The JACR article illustrates how Medicare’s Byzantine reimbursement structure puts barriers in the path of wider AI adoption. Although there have been some reimbursement victories such as NTAP, these have been temporary, and the fact that only one radiology AI algorithm has achieved a Category I CPT code must be a sobering thought to AI proponents.

Can You Believe the AI Hype?

Posted on July 5, 2023July 5, 2023 by Brian Casey

Can you believe the hype when it comes to marketing claims made for AI software? Not always. A new review in JAMA Network Open suggests that marketing materials for one-fifth of FDA-cleared AI applications don’t agree with the language in their regulatory submissions.

Interest in AI for healthcare has exploded, creating regulatory challenges for the FDA due to the technology’s novelty. This has left many AI developers guessing how they should comply with FDA rules, both before and after products get regulatory clearance.

This creates the possibility for discrepancies between products the FDA has cleared and how AI firms promote them. To investigate further, researchers from NYU Langone Health analyzed content from 510(k) clearance summaries and accompanying marketing materials for 119 AI- and machine learning (ML)-enabled devices cleared from November 2021 to March 2022. Their findings included:

Overall, AI/ML marketing language was consistent with 510(k) summaries for 80.67% of devices
Language was considered “discrepant” for 12.61% and “contentious” for 6.72%
Most of the AI/ML devices surveyed (63.03%) were developed for radiology use; these had a slightly higher rate of consistency (82.67%) than the entire study sample

The authors provided several examples illustrating when AI/ML firms went astray. In one case labeled as “discrepant,” a developer touted the “cutting-edge AI and advanced robotics” in its software for measuring and displaying cerebral blood flow with ultrasound. But the product’s 510(k) summary never discussed AI capabilities, and the algorithm isn’t included on the FDA’s list of AI/ML-enabled devices.

In another case labeled as “contentious,” marketing materials for an ECG mapping software application mention that it includes computation modeling and is a smart device, but require users to request a pamphlet from the developer for more information.

The Takeaway

So, can you believe the AI hype? This study shows that most of the time you can, with a consistency rate of 80.67% – not bad for a field as new as AI (a fact acknowledged in an invited commentary on the paper). But the study’s authors suggest that “any level of discrepancy is important to note for consumer safety.” And for a technology that already has trust issues, it’s probably best that developers not push the envelope when it comes to marketing.

H1 Radiology Recap

Posted on June 28, 2023June 26, 2023 by Brian Casey

That’s a wrap for the first half of 2023. Below are the top stories in radiology for the past 6 months, as well as some tips on what to look for in the second half of the year.

ChatGPT Takes Over Radiology – Our first mention of ChatGPT came in December 2022, but it was in the first half of 2023 that the generative AI algorithm really began to spread its wings in medical imaging. Researchers began finding new use cases for ChatGPT, GPT-4 passed the radiology board exam, and ChatGPT even contributed to a review article on itself. But rumblings of disaffection have been simmering, both about whether ChatGPT will measure up to the hype and about the overall impact of AI on society if its growth continues unfettered. H2 of 2023 will likely see radiology find more creative ways to leverage the model’s potential.

Radiology Bounces Back – After several crushing years in the wake of the COVID-19 pandemic, the first half brought welcome news to radiology on several fronts. The 2023 Match wrapped up with diagnostic radiology on top as the most popular medical specialty for medical students over the past 3 years. Radiology was one of the highest-compensated specialties in surveys from Medscape and Doximity, and even vendors got into the act, reporting higher revenue and earnings as supply chain delays cleared up. Will the momentum continue in the second half?

Burnout Looms Large – Even as salaries grow, healthcare is grappling with increased physician burnout. Realization is growing that burnout is a systemic problem – tied to rising healthcare volumes – that defies self-care solutions. Congressional legislation would boost residency slots 5% a year for 7 years, but is even this enough? Alternatively, could IT tools like AI help offload medicine’s more mundane tasks and alleviate workloads? Both questions will be debated in the back half of 2023.

Mammography Makes News – On the clinical side, breast imaging made headlines in the first half, led by the USPSTF’s decision to lower its recommended starting age for mammography screening to 40. DBT was also a hot topic, especially in studies pointing to the technology’s growing role as a tool for screening. The FDA even released its long-awaited policy setting a national standard for breast density reporting.

In-Person Shows Are Back – The pandemic took a wrecking ball to the trade show calendar, but things began to return to normal in the first half of 2023. Both ECR and HIMSS held meetings that saw respectable attendance, following up on a successful RSNA 2022. By the time SIIM 2023 rolled around in early June, the pandemic was a distant memory as radiology focused on the value of being together.

The Takeaway

As the second half of 2023 begins, all eyes will be on ChatGPT and whether a technology that’s mostly a curious novelty now can evolve into a useful clinical tool in the future.

Better Together at SIIM

Posted on June 18, 2023June 16, 2023 by Brian Casey

Humans have a deep-seated need for interpersonal contact, and understanding that need should guide not only how we structure our work relationships in the post-COVID era, but also our development and deployment of new technologies like AI in radiology.

That’s according to James Whitfill, MD, who gave Thursday’s opening address at SIIM 2023. Whitfill’s talk – which was followed by a raucous audience participation exercise – was a ringing demonstration that in-person meetings like SIIM still have relevance despite the proliferation of Zoom calls and remote work.

Whitfill, chief transformation officer at HonorHealth in Arizona and an internist at the University of Arizona, was chair of the SIIM board in 2020 when the society made the difficult decision to move its annual meeting to be fully online during the pandemic.

The experience led Whitfill to ponder whether technology designed to help us work and collaborate virtually was an adequate substitute for in-person interaction. Unfortunately, the research suggests otherwise:

Numerous studies have demonstrated the negative effect that the isolation of the COVID pandemic has had on adolescent mental health and academic performance
Loneliness can also have a negative effect on physical well-being, with a recent U.S. Surgeon General’s report finding that prolonged isolation is the health equivalent of smoking 15 cigarettes a day
Peer-reviewed studies have shown that people working in in-person collaborative environments are about 10% more productive and creative than those working virtually.

Whitfill’s talk was especially on-point given recent research indicating that workers across different industries who used AI were more lonely than those who didn’t, a phenomenon that shouldn’t be ignored by those planning radiology’s AI-based future.

That said, virtual technologies can still play a role in making access to information more equitable. Whitfill noted that some 160 people were following the SIIM proceedings entirely online, and they otherwise would not have been able to benefit from the meeting’s content.

To drive the point home, Whitfill then had audience members participate in a team-based Rochambeau competition that sent peals of laughter ringing through Austin Convention Center.

The Takeaway
Whitfill’s point was underscored repeatedly by SIIM 2023 attendees, who reiterated the value of interpersonal connections and networking at the conference. It’s ironic that a meeting devoted at least in part to intelligence that’s artificial has made us better appreciate relationships that are real.

AI Reinvigorates SIIM 2023

Posted on June 14, 2023June 14, 2023 by Brian Casey

AUSTIN – Before AI came along, the Society for Imaging Informatics in Medicine (SIIM) seemed to be a conference in search of itself. SIIM (and before it, SCAR) built its reputation on education and training for radiology’s shift to digital image management.

But what happens when the dog catches the truck? Radiology eventually fully adopted digital imaging, and that meant less need to teach people about technology they were already using every day.

Fast forward to the AI era, and SIIM seems to have found its new mission. Once again, radiology is faced with a transformative IT technology that few understand and even fewer know how to put into clinical practice. With its emphasis on education and networking, SIIM is a great forum to learn how to do both.

That’s exemplified by the SIIM keynote address on Wednesday, by Ziad Obermeyer, MD, a physician and researcher in machine learning at UC Berkeley who has published important research on bias in machine learning.

While not a radiologist, Obermeyer served up a fascinating talk on how AI should be designed and adopted to have maximum impact. His advice included:

Don’t design AI to perform the same tasks humans do already. Train algorithms to perform in ways that make up for the shortcomings of humans.
Training algorithms on medical knowledge from decades ago is likely to produce bias when today’s patient populations don’t match those of the past.
Access to high-quality data is key to algorithm development. Data should be considered a public good, but there is too much friction in getting it.

To solve some of these challenges, Obermeyer is involved in two projects, Nightingale Open Science to connect researchers with health systems, and Dandelion Health, designed to help AI developers access clinical data they need to test their algorithms.

The Takeaway

The rise of AI – particularly generative AI models like ChatGPT – has given SIIM a shot in the arm from a content perspective, and the return of in-person meetings plays to the conference’s strength as an intimate get-together where the networking and relationship-building is almost as important as the content. Please follow along with the proceedings of SIIM 2023 on our Twitter and LinkedIn pages.

When AI Goes Wrong

Posted on June 4, 2023June 4, 2023 by Brian Casey

What impact do incorrect AI results have on radiologist performance? That question was the focus of a new study in European Radiology in which radiologists who received incorrect AI results were more likely to make wrong decisions on patient follow-up – even though they would have been correct without AI’s help.

The accuracy of AI has become a major concern as deep learning models like ChatGPT become more powerful and come closer to routine use. There’s even a term – the “hallucination effect” – for when AI models veer off script to produce text that sounds plausible but in fact is incorrect.

While AI hallucinations may not be an issue in healthcare – yet – there is still concern about the impact that AI algorithms are having on clinicians, both in terms of diagnostic performance and workflow.

To see what happens when AI goes wrong, researchers from Brown University sent 90 chest radiographs with “sham” AI results to six radiologists, with 50% of the studies positive for lung cancer. They employed different strategies for AI use, ranging from keeping the AI recommendations in the patient’s record to deleting them after the interpretation was made. Findings included:

When AI falsely called a true-pathology case “normal,” radiologists’ false-negative rates rose compared to when they didn’t use AI (20.7-33.0% depending on AI use strategy vs. 2.7%)
AI calling a negative case “abnormal” boosted radiologists’ false-positive rates compared to without AI (80.5-86.0% vs. 51.4%)
Not surprisingly, when AI calls were correct, radiologists were more accurate with AI than without, with increases in both true-positive rates (94.7-97.8% vs. 88.3%) and true-negative rates (89.7-90.7% vs. 77.3%)

Fortunately, the researchers offered suggestions on how to mitigate the impact of incorrect AI. Radiologists had fewer false negatives when AI provided a box around the region of suspicion, a phenomenon the researchers said could be related to AI helping radiologists focus.

Also, radiologists’ false positives were higher when AI results were retained in the patient record versus when they were deleted. Researchers said this was evidence that radiologists were less likely to disagree with AI if there was a record of the disagreement occurring.

The Takeaway
As AI becomes more widespread clinically, studies like this will become increasingly important in shaping how the technology is used in the real world, and add to previous research on AI’s impact. Awareness that AI is imperfect – and strategies that take that awareness into account – will become key to any AI implementation.

AI Investment Shift

Posted on May 20, 2023May 19, 2023 by Brian Casey

VC investment in the AI medical imaging sector has shifted notably in the last couple years, says a new report from UK market intelligence firm Signify Research. The report offers a fascinating look at an industry where almost $5B has been raised since 2015.

VC investment in the AI medical imaging sector has shifted in the last couple years, with money moving to later-stage companies.

Total Funding Value Drops – Both investors and AI independent software vendors (ISVs) have noticed reduced funding activity, and that’s reflected in the Signify numbers. VC funding of imaging AI firms fell 32% in 2022, to $750.4M, down from a peak of $1.1B in 2021.

Deal Volume Declines – The number of deals getting done has also fallen, to 42 deals in 2022, off 30% compared to 60 in 2021. In imaging AI’s peak year, 2020, 95 funding deals were completed.

VC Appetite Remains Strong – Despite the declines, VCs still have a strong appetite for radiology AI, but funding has shifted from smaller early-stage deals to larger, late-stage investments.

HeartFlow Deal Tips Scales – The average deal size has spiked this year to date, to $27.6M, compared to $17.9M in 2022, $18M in 2021, and $7.9M in 2020. Much of the higher 2023 number is driven by HeartFlow’s huge $215M funding round in April; Signify analyst Sanjay Parekh, PhD, told The Imaging Wire he expects the average deal value to fall to $18M by year’s end.

The Rich Get Richer – Much of the funding has concentrated in a dozen or so AI companies that have raised over $100M. Big winners include HeartFlow (over $650M), and Cleerly, Shukun Technology, and Viz.ai (over $250M). Signify’s $100M club is rounded out by Aidoc, Cathworks, Keya Medical, Deepwise Shenrui, Imagen Technologies, Perspectum, Lunit, and Annalise.ai.

US and China Dominate – On a regional basis, VC funding is going to companies in the US (almost $2B) and China ($1.1B). Following them are Israel ($513M), the UK ($310M), and South Korea ($255M).

The Takeaway

Signify’s report shows the continuation of trends seen in previous years that point to a maturing market for medical imaging AI. As with any such market, winners and losers are emerging, and VCs are clearly being selective about choosing which horses to put their money on.

Tag: ImagingAI

Top 10 Radiology Stories of 2023

Economic Barriers to AI

Can You Believe the AI Hype?

H1 Radiology Recap

Better Together at SIIM

AI Reinvigorates SIIM 2023

When AI Goes Wrong

AI Investment Shift

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