Author: Brian Casey

Top 10 Radiology Stories of 2024

Posted on by Brian Casey

What were the top 10 radiology stories of 2024 in The Imaging Wire? This year’s top 10 list as measured by reader views demonstrates the fascinating new developments going on every day in medical imaging.

  1. Radiologist Shortage Looms: A July report painted a gloomy picture of the demographic crush facing radiology as the U.S. population ages and imaging volumes rise, but the number of radiologists remains static.
  1. Study Shows AI’s Economic Value: A March study in JACR tackled arguments against AI’s economic value, demonstrating AI’s ability to both improve radiologist efficiency and also drive new revenues for imaging facilities. 
  1. Radiology’s Private-Practice Squeeze: It’s no secret that U.S. radiology’s traditional private-practice model has been slowly fading away, but a study published in AJR in June illustrated the magnitude of the shift. The number of radiologist-affiliated and radiologist-only practices has dropped, even as the total number of U.S. radiologists has gone up.
  1. Radiologist Pay Rebounds: Radiologist pay grew 5.6% and radiology moved up one notch in a May survey of highest-paid U.S. medical specialties for 2023. Physician salaries generally rebounded last year after a decline in 2022.
  1. FDA Keeps Pace on AI Approvals: The FDA in August updated its list of AI- and machine learning-enabled medical devices that have received regulatory authorization, showing the agency keeping a brisk pace of authorizations.
  1. Is Radiology’s AI Edge Fading? FDA figures from May hinted that radiology’s AI edge might be fading, at least when it comes to the specialty’s share of AI-enabled medical devices being granted regulatory authorization.
  1. Is Head CT Overused in the ED? A study in March suggested that head CT could be overused in the emergency department for patients presenting with conditions like headache and dizziness, as researchers found a big increase in CT angiography utilization.
  1. AI Speeds Up MRI Scans: Researchers in March found that AI-based data reconstruction sped up MRI scans and helped their hospital avoid buying a new scanner by improving throughput. 
  1. 6 Solutions to the RT Shortage: A new report published in July from the ASRT and other groups confirmed the shortage of radiologic technologists is severe, but offers some solutions. 
  1. MASAI Gets Even Better at ECR 2024: At ECR 2024, researchers in the MASAI study presented final data indicating that AI could have an even bigger impact on mammography screening than we thought.

The Takeaway
The Imaging Wire’s list of top 10 articles for 2024 shows that bread-and-butter issues like the radiologist shortage and physician reimbursement continue to be top of mind for our readers. The use of AI in radiology is a close second, and our readers can be assured that we will follow all of these issues closely in 2025.

Mobile Mammography’s Value

Posted on by Brian Casey

Despite the proven value of breast screening, compliance rates still aren’t as high as they should be. A new study in Clinical Breast Cancer shows how mobile mammography can improve screening adherence – especially among groups traditionally underserved in the healthcare system.

Estimates of mammography compliance vary – the American Cancer Society estimates that the overall U.S. breast screening rate held steady at 64-66% from 2000 to 2018. 

  • But a variety of factors can influence screening rates, from race to income to location.

Mobile mammography is an obvious solution that brings the imaging test to women rather than requiring them to travel. 

  • But some questions have persisted about mobile screening, such as whether it might cannibalize facility-based mammography programs, which have higher fixed costs. 

In the new study, researchers from the Harvey L. Neiman Health Policy Institute reviewed CMS claims data for 2.6M eligible women from 2004 to 2021. 

Researchers found …  

  • 50% of women had received a screening mammogram.
  • Only 0.4% used mobile mammography, but rates were higher in rural areas (1%) compared to large cities (0.3%) and small towns (0.4%).
  • American Indian or Alaska Native race was the factor most predictive for receiving mobile mammography (OR=5.5).
  • Other predictive factors included residence in a rural geography (OR=3.3), as well as in a community with lower income (OR=1.4).
  • Mobile mammography did not cannibalize facility-based mammography, based on data from heat maps showing utilization of both types of service.

Researchers concluded that mobile mammography can reduce health disparities by bringing imaging technology to underserved communities that might not otherwise have access to it. 

  • The findings echo a study earlier this year in which mobile mammography was also found to benefit the environment by reducing greenhouse gas emissions that occur when patients have to travel to medical facilities for screening.

The Takeaway

It may seem like a no-brainer to bring imaging to the people who need it, but the new study provides valuable evidence that the practice works on a national scale. Increased use of mobile imaging is an important tool for addressing persistent disparities in access to care. 

AI As Malpractice Safety Net

Posted on by Brian Casey

One of the emerging use cases for AI in radiology is as a safety net that could help hospitals avoid malpractice cases by catching errors made by radiologists before they can cause patient harm. The topic was reviewed in a Sunday presentation at RSNA 2024. 

Clinical AI adoption has been held back by economic factors such as limited reimbursement and the lack of strong return on investment. 

  • Healthcare providers want to know that their AI investments will pay off, either through direct reimbursement from payors or improved operational efficiency.

At the same time, providers face rising malpractice risk, with a number of recent high-profile legal cases.

  • For example, a New York hospital was hit with a $120M verdict after a resident physician working the night shift missed a pulmonary embolism. 

Could AI limit risk by acting as a backstop to radiologists? 

  • At RSNA 2024, Benjamin Strong, MD, chief medical officer at vRad, described how they have deployed AI as a QA safety net. 

vRad mostly develops its own AI algorithms, with the first algorithm deployed in 2015. 

  • vRad is running AI algorithms as a backstop for 13 critical pathologies, from aortic dissection to superior mesenteric artery occlusion.

vRad’s QA workflow begins after the radiologist issues a final report (without using AI), and an algorithm then reviews the report automatically. 

  • If discrepancies are found the report is sent to a second radiologist, who can kick the study back to the original radiologist if they believe an error has occurred. The entire process takes 20 minutes. 

In a review of the program over one year, vRad found …

  • Corrections were made for about 1.5k diagnoses out of 6.7M exams.
  • The top five AI models accounted for over $8M in medical malpractice savings. 
  • Three pathologies – spinal epidural abscess, aortic dissection, and ischemic bowel due to SMA occlusion – would have amounted to $18M in payouts over four years.
  • Adding intracranial hemorrhage and pulmonary embolism creates what Strong called the “Big Five” of pathologies that are either the most frequently missed or the most expensive when missed.

The Takeaway

The findings offer an intriguing new use case for AI adoption. Avoiding just one malpractice verdict or settlement would more than pay for the cost of AI installation, in most cases many times over. How’s that for return on investment?

RSNA 2024 Video Highlights

Posted on by Brian Casey

Last week’s RSNA 2024 meeting saw a major bounce in attendance, with early numbers indicating an 18% jump in the number of radiology professionals wandering the halls of McCormick Place. The increase brought total attendance at midweek to 40k. 

As in past years, AI dominated the discussion, both in the presentation rooms and on the exhibit floor. Researchers presented the latest findings on AI’s ability to aid radiologists, while vendors showcased new algorithms for use cases from mammography screening to fracture detection. New technologies like foundation models for AI training bubbled under the surface and promise to have a major impact in years to come.

It was our privilege to speak with many of the most interesting vendors exhibiting at RSNA 2024, from multinational vendors to small but promising start-ups.

We hope you enjoy watching our coverage as much as we enjoyed producing it! Check out the links below or visit the Shows page on our website.

How Are Doctors Using AI?

Posted on by Brian Casey

How are healthcare providers who have adopted AI really using it? A new Medscape/HIMSS survey found that most providers are using AI for administrative tasks, while medical image analysis is also one of the top AI use cases. 

AI has the potential to revolutionize healthcare, but many industry observers have been frustrated with the slow pace of clinical adoption. 

  • Implementation challenges, regulatory issues, and lack of reimbursement are among the reasons keeping more healthcare providers from embracing the technology.

But the Medscape/HIMSS survey shows some early successes for AI … as well as lingering questions. 

  • Researchers surveyed a total of 846 people in the U.S. who were either executive or clinical leaders, practicing physicians or nurses, or IT professionals, and whose practices were already using AI in some way.

The top four tasks for which AI is being used were administrative rather than clinical, with image analysis occupying the fifth spot … 

  1. Transcribing patient notes (36%). 
  2. Transcribing business meetings (32%).
  3. Creating routine patient communications (29%).
  4. Performing patient record-keeping (27%).
  5. Analyzing medical images (26%).

The survey also analyzed attitudes toward AI, finding …

  • 57% said AI helped them be more efficient and productive.
  • But lower marks were given for reducing staff hours (10%) and lowering costs (31%).
  • AI got the highest marks for helping with transcription of business meetings (77%) and patient notes (73%), reviewing medical literature (72%), and medical image analysis (70%).

The findings track well with developments at last week’s RSNA 2024, where AI algorithms dedicated to non-clinical tasks like radiology report generation, scheduling, and operation analysis showed growing prominence. 

  • Indeed, many AI developers have specifically targeted the non-clinical space, both because commercialization is easier (FDA authorization is not typically needed) and because doctors often say they need more help with administrative rather than clinical tasks.

The Takeaway

While it’s easy to be impatient with AI’s slow uptake, the Medscape/HIMSS survey shows that AI adoption is indeed occurring at medical practices. And while image analysis was radiology’s first AI use case, speeding up workflow and administrative tasks may end up being the technology’s most impactful application.

RSNA Goes All-In on AI

Posted on by Brian Casey

CHICAGO – It’s been AI all the time this week at RSNA 2024. From clinical sessions packed with the latest findings on AI’s utility to technical exhibits crowded with AI vendors, artificial intelligence and its impact on radiology was easily the hottest trend at McCormick Place.

Radiology greeted AI with initial skepticism when the first applications like IBM Watson were introduced at RSNA around a decade ago.

  • But the field’s attitude has been evolving to the point where AI is now being viewed as perhaps the only technology that can save the discipline from the vicious cycle of rising exam volume, falling reimbursement, and pervasive levels of burnout.

RSNA telegraphed the shift last year by announcing that Stanford University’s Curtis Langlotz, MD, PhD, would be RSNA 2024 president. 

  • Langlotz is one of the most respected AI researchers and educators in radiology, and even coined the phrase that while AI would not replace radiologists, radiologists with AI would replace those without it. 

In his president’s address, Langlotz echoed this theme, painting a picture of a future radiology in which humans and machines collaborate to deliver better patient care than either could alone.

  • Langlotz’s talk was followed by a presentation by another prominent AI luminary – Nina Kottler, MD, of Radiology Partners.

Kottler took on the concerns that many in radiology (and in the world at large) have about AI as a disruptive force in a field that cherishes its traditions.

  • She advised radiology to take a leading role in AI adoption, repeating a famous quote that the best way to predict the future is to create it yourself. 

What were the other trends besides AI at RSNA 2024? They included…

  • Photon-counting CT, which is likely to see new market entrants in 2025.
  • Total-body PET, with PET scanners that have extra-long detector arrays.
  • Theranostics, a discipline that integrates diagnosis and therapy and promises to breathe new life into SPECT.
  • CT colonography and CCTA, which will see positive reimbursement changes in 2025.
  • Continued growth of CT lung screening, especially as a tool for opportunistic screening of other conditions.
  • Continued expansion of AI for breast screening.

The Takeaway

The RSNA meeting has been called radiology’s Super Bowl and World Cup all rolled into one, and this year didn’t disappoint. RSNA 2024 showed that radiology is prepared to fully embrace AI – and a future in which humans and machines collaborate to deliver better patient care.

Mammo AI Kicks Off RSNA 2024

Posted on by Brian Casey

Welcome to RSNA 2024! This year’s meeting is starting with a bang, with two important sessions highlighting the key role AI can play in breast screening. 

Sunday’s presentations cap a year that’s seen the publication of several large studies demonstrating that AI can improve breast cancer screening while potentially reducing radiologist workload. 

  • That momentum is continuing at RSNA 2024, with morning and afternoon sessions on Sunday dedicated to mammography AI. 

Some findings from yesterday’s morning session include … 

  • Two AI algorithms were better than one when supporting radiologists in breast screening, with cancer detection ratios relative to historic performance rising from 0.97 to 1.08 with one AI to 1.09 to 1.14 with two algorithms.
  • ScreenPoint Medical’s Transpara algorithm was able to prioritize the worklist for 57% of breast screening exams by assigning risk scores to mammograms, helping reduce report turnaround times. 
  • iCAD’s ProFound AI software helped radiologists detect 7.8% more breast cancers on DBT exams, and cancers were detected at an earlier stage. 
  • Applying AI for breast screening to a racially diverse population yielded evenly distributed performance improvements.

Meanwhile, the Sunday afternoon session also included significant mammography AI presentations, such as …

  • A hybrid screening strategy – with suspicious breast cancer cases only recalled if the AI exhibits high certainty – reduced workload 50%. 
  • Lunit’s Insight DBT AI showed potential to reduce interval cancer rates in DBT screening by identifying 27% of false-negative and 36% of interval cancers.
  • In the ScreenTrustCAD trial in Sweden, using Lunit’s Insight MMG algorithm to replace a double-reading radiologist reduced workload 50% with comparable cancer detection rates.
  • A German screening program found that ScreenPoint Medical’s Transpara AI boosted the cancer detection rate by 8.7% (from 0.68% to 0.74%), with 8.8% of cancers solely detected by AI.
  • Researchers took a look back at abnormality scores from three commercially available AI algorithms after cancer diagnosis, finding evidence that cancers could be detected earlier. 

The Takeaway

Breast screening seems to be the clinical use case where radiologists need the most help, and Sunday’s sessions show the progress AI is making toward achieving that reality. 

Be sure to check back on our X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago. And if you see us on the floor of McCormick Place, stop and say hello!

How Should AI Be Monitored?

Posted on by Brian Casey

Once an AI algorithm has been approved and moves into clinical use, how should its performance be monitored? This question was top of mind at last week’s meeting of the FDA’s new Digital Health Advisory Committee.

AI has the potential to radically reshape healthcare and help clinicians manage more patients with fewer staff and other resources. 

  • But AI also represents a regulatory challenge because it’s constantly learning, such that after a few years an AI algorithm might be operating much differently from the version first approved by the FDA – especially with generative AI. 

This conundrum was a point of discussion at last week’s DHAC meeting, which was called specifically to focus on regulation of generative AI, and could result in new rules covering all AI algorithms. (An executive summary that outlines the FDA’s thinking is available for download.)

Radiology was well-represented at DHAC, understandable given it has the lion’s share of authorized algorithms (73% of 950 devices at last count). 

  • A half-dozen radiology AI experts gave presentations over two days, including Parminder Bhatia of GE HealthCare; Nina Kottler, MD, of Radiology Partners; Pranav Rajpurkar, PhD, of Harvard; and Keith Dreyer, DO, PhD, and Bernardo Bizzo, MD, PhD, both of Mass General Brigham and the ACR’s Data Science Institute.  

Dreyer and Bizzo directly addressed the question of post-market AI surveillance, discussing ongoing efforts to track AI performance, including … 

The Takeaway

Last week’s DHAC meeting offers a fascinating glimpse at the issues the FDA is wrestling with as it contemplates stronger regulation of generative AI. Fortunately, radiology has blazed a trail in setting up structures like ARCH-AI and Assess-AI to monitor AI performance, and the FDA is likely to follow the specialty’s lead as it develops a regulatory framework.

Non-Physicians Are Reading More Medical Images

Posted on by Brian Casey

Non-physician practitioners are reading more medical images in U.S. medical offices. That’s according to a new study in JACR by researchers who found that the share of images interpreted by NPPs has doubled in the last 10 years. 

In the U.S., radiologists consider themselves to be the primary interpreters of medical images, but inroads have been made not only by other physicians but also by non-physician practitioners like nurses and physician assistants. 

  • NPPs are supposed to receive specialized training in image interpretation, but radiologists question whether such training is adequate, especially compared to the years of training that radiologists receive. 

Previous research has documented the rise in NPP image interpretation, but the new study takes a longer view, examining the period 2013-2022. 

  • It also specifically focuses on the medical office setting, where it’s believed NPP interpretation is growing faster than in hospitals, where radiologists still dominate interpretations. 

In their analysis of Medicare claims, researchers from the ACR’s Harvey L. Neiman Health Policy Institute found … 

  • NPPs’ share of office-based image interpretations grew 9% annually (2.5% to 5.5%).
  • Growth rates varied by modality, with MRI growing at 9.9% annually, followed by CT (9.4%), ultrasound (9.4%), radiography (8.9%), and nuclear medicine (7.2%).
  • Despite the growth, just 5.6% of NPPs were interpreting images.
  • By specialty, the share of NPP interpretation was most common with primary care (40%) and orthopedic offices (34%).

The researchers also tracked variability in NPP interpretation rates by state, finding the highest rates ( ~13%) in Western states with large rural areas like Montana, Alaska, and Idaho, where presumably there are fewer radiologists available to read images. 

The Takeaway

The findings provide a good news/bad news look at non-physician image interpretation. The good news for radiologists is that NPP interpretation is still pretty rare; the bad news is that rates are growing quickly. And given the ongoing radiologist shortage, there is sure to be continuing pressure to allow allied health staff to read images on their own.

Will Congress Stop Medicare Cuts?

Posted on by Brian Casey

Radiologists find themselves once again in a familiar position, facing CMS cuts in Medicare and Medicaid physician payments for 2025. A new analysis by revenue cycle management company Healthcare Administrative Partners details the impact of the reductions, as well as other reimbursement changes set to take effect next year. 

CMS has been driving down radiology reimbursement for years, a trend widely seen as part of the agency’s effort to shift funding from medical specialties to primary care. 

  • That’s having an impact on physician pay, as a study last week found that private-practice diagnostic radiologists have seen inflation-adjusted salaries decline at a -1% annual rate since 2014. 

That trend is set to continue in 2025, with CMS publishing its final rule for the Medicare Physician Fee Schedule that affirms most of the changes it proposed in July. In the new article, HAP’s Sandy Coffta unpacks the changes, which include … 

  • A new conversion factor of $32.3465 (down from $33.2875).
  • Payment reductions of -2.8% for radiology and nuclear medicine, and -4.8% for interventional radiology.

But not all of the changes are negative. Other 2025 policies that affect radiology include …

  • Reimbursement for CT colonography for Medicare beneficiaries at a rate of $108.68 for the professional component.
  • New codes for reporting MRI safety procedures.
  • New quality category measures in the Merit-based Incentive Payment System.

CMS proposed similar cuts last year, but Congress swooped in at the last minute to roll them back with the Consolidated Appropriations Act, which applied a positive 2.93% upward adjustment. 

  • Several bills in Congress now would likewise stave off the 2025 reductions (H.R. 2474 and H.R. 10073), but time is running out to pass them before the current Congressional session expires on January 3, 2025. 

The Takeaway

Will Congress once again ride to the rescue and stave off Medicare reimbursement cuts, as it did a year ago? Or will things be different this time, given the political turbulence that’s shaking Washington, DC? We’ll find out in a few weeks.

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