Author: Brian Casey

SIIM 2025 Video Highlights

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine convened in Portland, Oregon, with members of radiology’s imaging IT community joining together to discuss the latest trends in enterprise imaging, AI, and more. 

As with other recent radiology meetings, AI dominated the discussion at SIIM 2025. But AI’s potential to revolutionize radiology has been tempered by nagging concerns about slow clinical adoption and questionable return on investment for healthcare providers.

Regulatory turbulence is also a concern, highlighted by recent changes implemented by the Trump Administration at the FDA. Some industry observers have speculated that AI approvals have slowed down, while others point out that the FDA – which has lagged other countries in approving new AI algorithms – perhaps might benefit from a fresh approach in how it regulates AI.

The Takeaway 

In the end, SIIM 2025 can be chalked up as another success for the organization. While attendance seemed to be down slightly (most likely due to the West Coast location and pre-Memorial Day timing), the society pointed out that the number of vendor exhibitors at SIIM 2025 exceeded 100 for the first time in years – a sure sign of a healthy imaging IT industry. 

Check out our SIIM 2025 videos below or visit the Shows page on our website, as well as our YouTube and LinkedIn pages, and keep an eye out for our next Imaging Wire newsletter on Thursday.

Imaging Workload Jumps with Higher Use of CT, MRI

Posted on by Brian Casey

Radiology’s shift to more advanced modalities like CT and MRI is increasing the burden on radiologists to interpret more complex exams. A new study in JACR documents the trend, finding that radiologist workload for inpatient imaging has risen sharply over the last 10 years. 

Like many physicians, radiologists are feeling burned out from rising patient workload, personnel shortages, and declining reimbursement. 

  • But radiology has the added burden of being one of healthcare’s most technology-focused specialties, with new imaging modalities giving them cooler tools to work with, but at the cost of steadily increasing exam complexity.

Researchers from Brigham and Women’s Hospital have been tracking inpatient imaging utilization for the past 40 years, and the new paper provides the latest update. 

  • They calculated inpatient imaging volume at Brigham and Women’s from 2012 to 2023, during which 896k imaging exams were performed.  

Results for the study were as follows …

  • Total annual inpatient imaging volume grew 17% over 10 years (102k to 119k exams).
  • Total imaging exams per patient admission (adjusted by case mix and disease severity) fell 20% due to declines in X-ray, ultrasound, and nuclear medicine.
  • But imaging exams per patient admission grew for CT (19%) and MRI (21%).
  • Leading to growth in CT and MRI’s combined share of all radiology global RVUs (62% to 75%).
  • Hospital length of stay rose 32% (5.6 to 7.4 days), possibly due to the COVID-19 pandemic. 

What does it all mean? Basically, the number of inpatient imaging exams per patient is declining when adjusted for disease severity, but radiologists are still having to work harder because the studies are more complex. 

  • Imaging could also be shifting from the inpatient setting to outpatient centers due to reimbursement changes aimed at shifting exams to lower-cost settings than hospitals.

One big question with the new study is the degree to which the COVID-19 pandemic skewed the results compared with previous years. 

  • The pandemic may have spurred more use of CT, especially given its value in providing a definitive diagnosis of SARS-CoV-2 infection. 

The Takeaway

If you feel like you’re working harder than ever, the new findings show that you’re not crazy. And given radiology’s breakneck pace of innovation, it’s not likely the trends revealed in the new study will let up any time soon.

MRI in Paradise – News from ISMRM 2025

Posted on by Brian Casey

The global MRI community this week traveled to paradise to convene its annual meeting of the International Society for Magnetic Resonance in Medicine. If you were one of the lucky ones to be in attendance in Honolulu, Hawaii for ISMRM 2025, you were treated to some of the latest news in radiology’s most powerful modality. 

As has been the case at other radiology meetings, AI took center stage in Honolulu. 

  • AI has multiple use cases in MRI, from helping radiologists interpret images more efficiently to accelerating scans and upscaling lower-field images to resemble high-field exams.

Just a few of the news highlights from ISMRM 2025 are below …

  • Using AI to interpret prostate MRI reduced reading times by 48% (250 to 120 seconds) while improving the diagnostic performance of both experienced and less experienced radiologists. 
  • AI of thyroid T2-weighted neck MRI scans demonstrated good accuracy (87%) for nodules larger than 1 cm, indicating a possible role for screening and monitoring.
  • Researchers presented progress in creating brain charts of white matter based on MRI scans of 24k cognitively healthy people that can be used to track normal and abnormal brain development.
  • Brain MRI showed that lower brain volumes in people with coronary artery disease were associated with worse aerobic fitness and higher BMI, revealing a link between cardiovascular and brain health. 
  • Chinese researchers showed their work on PMEEN, a multimodality brain scanner that combines PET, MRI, EEG, eye-tracking, and functional near-infrared spectroscopy. 
  • A Spanish team demonstrated research on a low-field PET/MRI scanner with focused ultrasound capability for therapeutic applications.
  • AI could be used during abbreviated breast MRI screening scans to convert women mid-exam to a full MRI protocol if abnormalities are detected.
  • 7T MRI was used to detect iron deposits in the brain, which could be a marker for Alzheimer’s disease.
  • MRI with an ultrashort echo time protocol could be an alternative to CT for following up lung nodules.
  • Researchers presented a deep learning-based approach to generating synthetic contrast-like MR images without gadolinium. 
  • MGH researchers showed progress in developing a 136mT portable MRI scanner for bedside brain scanning of preterm neonates.

The Takeaway

The rapid proliferation of news about AI-based MRI at ISMRM 2025 suggests its own vision of paradise – a world in which MRI can be deployed more widely than ever before, where radiologists with AI assistance detect disease in many cases before symptoms even occur. We can only dream.

AI Boosts DBT in Detecting More Breast Cancer

Posted on by Brian Casey

A real-world study of AI for DBT screening found that AI-assisted mammogram interpretation nearly doubled the breast cancer detection rate. Radiologists using iCAD’s ProFound AI software saw sharp improvements across multiple metrics. 

Mammography screening has quickly become one of the most promising use cases for AI. 

  • Multiple large-scale studies published in 2024 and 2025 have documented improved radiologist performance when using AI for mammogram interpretation, with the largest studies performed in Europe.

Another new technology changing mammography screening is digital breast tomosynthesis, which is being rapidly adopted in the U.S. 

  • DBT use in Europe is occurring more slowly, so questions have arisen about whether AI’s benefits for 2D mammography would also be found with 3D systems.

To investigate this question, researchers writing in Clinical Breast Cancer tested radiologist performance for DBT screening before and after implementation of iCAD’s ProFound V2.1 AI algorithm in 2020 at Indiana University. 

  • Interestingly, the pre-AI period included use of iCAD’s older PowerLook CAD software. 

Across the 16.7k DBT cases studied, those with AI saw …

  • A sharp improvement in cancer detection rate per 1k exams (6.1 vs. 3.7).
  • A decline in the abnormal interpretation rate (6.5% vs. 8.2%).
  • Higher PPV1 (rate that abnormal mammograms would be positive) (8.8% vs. 4.2%).
  • Higher PPV3 (rate that biopsies would be positive) (57% vs. 32%). 
  • Higher specificity (94% vs. 92%).
  • No statistically significant change in sensitivity.

The findings on sensitivity are curious given AI’s positive impact on other interpretation metrics.

  • Researchers postulated that there was higher breast cancer incidence in the post-AI implementation period, which could have been caused by AI finding cancers that were missed in the period without AI.

The Takeaway

The radiology world has seen multiple positive studies on AI for mammography, but most of these have come from Europe and involved 2D mammography not DBT. The new results suggest that AI’s benefits will also transfer to DBT, the technology that’s becoming the standard of care for breast screening in the U.S.

How Do Patients Feel about Mammo AI?

Posted on by Brian Casey

As radiology moves (albeit slowly) to adopt clinical AI, how do patients feel about having their images interpreted by a computer? Researchers in a new study in JACR queried patients about their attitudes regarding mammography AI, finding that for the most part the jury is still out. 

Researchers got responses to a 36-question survey from 3.5k patients presenting for breast imaging at eight U.S. practices from 2023-2024, finding …

  • The most common response to four questions on general perceptions of medical AI was “neutral,” with a range of 43-51%. 
  • When asked if using AI for medical tasks was a bad idea, more patients disagreed than agreed (28% vs. 25%). 
  • Regarding confidence that medical AI was safe, patients were more dubious, with higher levels of disagreement (27% vs. 20%).
  • When asked if medical AI was helpful, 43% were neutral but positive attitudes were higher (35% vs. 19%).

The Takeaway

Much like clinicians, patients seem to be taking a wait-and-see attitude toward mammography AI. The new survey does reveal fault lines – like privacy and equitability – that AI developers would do well to address as they work to win broader acceptance for their technology. 

We’re testing a new format today – let us know if you prefer two shorter Top Stories or one longer Top Story with this quick survey!

Function Buys Ezra to Add Labs to Screening

Posted on by Brian Casey

In a major development in the wellness-screening segment, diagnostic lab screening company Function Health acquired full-body MRI firm Ezra. The companies plan to offer wellness screening that combines lab tests with imaging.

Ezra launched in 2018 with an initial focus on prostate MRI but soon expanded into full-body MRI screening.

  • The company has developed AI-enhanced image acceleration algorithms to acquire MRI scans in shorter time slots, enabling it to drive down costs to consumers.

Ezra’s scans are currently available at 100 U.S. locations with the goal of 1k sites in coming months (the company doesn’t run its own centers, but rather partners with existing imaging providers like AMRIC Health). 

  • Function Health has a similar strategy but in the clinical diagnostics space, offering blood tests available through some 2.2k Quest Diagnostics locations.

Function and Ezra believe that combining lab tests with imaging will support a new level of wellness screening that when coupled with AI will be even more predictive.

The Takeaway

The combination of Function Health and Ezra is an interesting wrinkle in the wellness screening space that promises to make screening even more comprehensive by acquiring both lab and imaging data. The question is whether other screening providers will feel compelled to follow suit.

We’re testing a new format today – let us know if you prefer two shorter Top Stories or one longer Top Story with this quick survey!

Imaging Cost Variation Narrows after Transparency Rule

Posted on by Brian Casey

Why do costs for medical imaging procedures vary so much between U.S. hospitals? This is one of radiology’s most enduring mysteries, but a new study in JACR shows that variation may be narrowing in the wake of federal transparency rules. 

It’s common knowledge that patients (and payors) can be charged differently for the same healthcare procedure based on the facility where it’s conducted. 

  • Previous studies have found that patients are surprisingly unclear on how much their imaging exams will cost them.

To clear things up, CMS in 2021 rolled out transparency rules for medical procedures that require healthcare providers to share cost information with patients. 

  • The rules specified 300 “shoppable” services, including 13 imaging procedures like mammography, abdominal ultrasound, and head CT and MRI scans.

But has the rule been effective in reducing cost variation? 

  • The new study tackles that question head-on, analyzing cost changes from 2023 to 2024 for three common outpatient imaging exams – MRI brain studies with and without contrast, chest radiographs, and nuclear medicine gastric emptying exams.

Researchers tracked prices for the three exams at 26 U.S. pediatric hospitals, finding …

  • The variation coefficient for all three procedures declined 19% (from 27% to 21%).
  • The greatest decline in variation was for nuclear medicine gastric emptying (-7.2%) while the smallest was for chest radiography (-2.2%).
  • Overall prices rose 6.7% for payor-specific negotiated rates even as variation declined.
  • Prices increased 7.7% for nuclear medicine gastric emptying, 6.6% for brain MRI, and 2.6% for chest radiography. 

Among the five commercial payors tracked (Aetna, Blue Cross Blue Shield, Cigna, Humana, and UnitedHealth), BCBS moved from having the second-lowest prices in 2023 to the lowest in 2024, while Humana was the highest-priced insurer in both years. 

The Takeaway

The new results are a classic good news/bad news scenario for radiology. While the reduction in price variation is a positive trend, it appears that the growth in healthcare costs is an inexorable force that even the best-intended legislation can’t derail. 

RadPartners + Envision Consolidate Imaging Services

Posted on by Brian Casey

In a stunning consolidation of the imaging services segment, Radiology Partners has agreed to take over radiology contracts currently held by debt-laden national medical group Envision Healthcare. The agreement could bring up to 100 imaging sites and hundreds of radiologists into the RadPartners fold. 

The takeover is a remarkable comedown for Envision, which was once one of the largest national medical practices in the U.S. and employed some 25k physicians when it was acquired in 2018 by private equity giant KKR. 

  • Envision’s business crossed multiple medical specialties, with its radiology operation at one point employing 800 radiologists who performed over 10 million reads per year. 

But Envision struggled under a $5.3B debt load imposed by the KKR buyout, and eventually filed for Chapter 11 bankruptcy protection in 2023 in a move that also included spinning off its ambulatory surgery business. 

  • Many industry observers have viewed Envision’s rise and fall as a cautionary tale illustrating the perils of private-equity investment in American medicine.

Radiology Partners itself has evolved into the giant of the imaging services segment as it rolls up local radiology practices into a massive national network. Under the agreement with Envision, RP will … 

  • Take over Envision’s contracts with some 95 client sites, including teleradiology. 
  • Potentially bring some 400 Envision radiologists onboard (assuming they want to join RP).

The question is, how many Envision radiologists will choose to go with the contracts and join Radiology Partners? 

  • Speculation on industry bulletin board RadHQ.net proposes that Envision radiologists will be offered new contracts with RP – contracts that they can take or leave.

The Takeaway

Radiology Partners’ takeover of Envision’s radiology contracts will only enhance RP’s dominance of the imaging services market, which is already significant. While that may be good news to RP’s investors, it probably won’t be encouraging to those worried about the inexorable corporatization of radiology. 

Opportunistic Calcium Scoring Shifts to Abdomen

Posted on by Brian Casey

Most of the recent research on calcium scoring has focused on calcium in the coronary arteries and its link to cardiovascular disease. But a new study in American Heart Journal used abdominal CT scans with AI analysis for opportunistic measurement of abdominal aortic calcium to predict cardiac events – possibly earlier than CAC scores.

CT-derived CAC scores have become a powerful tool for predicting cardiovascular disease, helping physicians determine when to begin preventive therapy with treatments like statins.

  • CAC scores can be generated from dedicated cardiac CT scans, or even lung screening exams as part of a two-for-one test. 

Abdominal CT represents another promising area for calculating calcium. 

  • Previous research has found that atherosclerosis in the abdominal aorta may occur before its development in the coronary arteries, creating the opportunity to detect calcium earlier. 

Researchers from NYU Langone did just that in the new study, performing abdominal and cardiac CT scans in 3.6k patients and using an AI algorithm they developed in partnership with Visage Imaging to calculate AAC. They found that over an average three-year follow-up period … 

  • AI analysis of AAC severity was positively associated with CAC.
  • AAC could be used to rule out the presence of CAC relative to two versions of the PREVENT score (AUC=0.701 and 0.7802). 
  • The presence of AAC was associated with a higher adjusted risk of major adverse cardiovascular events (HR=2.18).
  • A doubling of the AAC score was linked to 11% higher risk of MACE.

The Takeaway

The new results are an exciting demonstration of opportunistic screening’s value, especially given the volume of abdominal CT scans performed annually. AI analysis of routinely acquired abdominal CT could give radiologists a tool for detecting heart disease risk even earlier than what’s possible with CAC scoring.

MRI Recon Gets Real with AI-Driven Protocols

Posted on by Brian Casey

AI-based data reconstruction for MRI scans took a step forward this week with studies showing how to generate 3T-like images from ultralow-field scanners, and improve scanner efficiency by cutting energy consumption.

MRI is radiology’s premier modality, but MRI scanners are cumbersome to install and expensive to operate. 

  • Ultralow-field scanners could help but some believe they lack the image quality for some clinical applications. 

Enter AI-based image reconstruction. Deep learning protocols are being developed for a wide range of imaging modalities, from PET to CT to MRI. 

  • These algorithms take images acquired with lower-quality input data – be it less CT radiation dose or lower MRI field strength – and upscale them to resemble full-fidelity images.

This trend is illustrated by research published this week in Radiology in which researchers tested a generative adversarial network algorithm called LowGAN for reconstructing data acquired on Hyperfine’s Swoop 0.064T portable ultralow-field MRI scanner.

  • Their goal was to enable Swoop to generate images resembling those acquired on a 3T system. 

After training LowGAN on paired 3T and 0.064T images, they tested the algorithm in 50 patients with multiple sclerosis and further validated it with a separate 13-patient cohort. They then judged LowGAN against several measures of MR image quality, finding that it …

  • Showed the biggest improvement on synthetic FLAIR and T1 images.
  • Improved conspicuity of white matter lesions, without introducing false lesions.
  • Increased consistency of cortical and subcortical volume measurements with 3T images.
  • But was unable to reveal brain lesions that were missed in the original low-field scans. 

AI-based data reconstruction also has environmental implications. Medical imaging is a major contributor to greenhouse gas emissions, and anyone who’s managed an MRI operation knows how much energy these massive scanners consume. 

  • A second paper published this week in Radiology described how MRI acceleration – scans acquired at a faster speed and then reconstructed for better image quality – reduced energy use, lowering carbon emissions while boosting imaging capacity. 

Researchers tried three techniques for speeding MRI acquisition – parallel acceleration, simultaneous multi-slice, and a deep learning algorithm. 

  • All three reduced energy consumption 21% to 65% and increased daily capacity by one to seven scanning slots, with deep learning showing the biggest effect.

The Takeaway

The new papers demonstrate an exciting future in which less powerful data acquisition technologies can be upscaled with AI to produce images that more closely resemble state-of-the-art scanning. The benefits will be enjoyed by both patients and the planet.

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