Tag: AI

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. 

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Integrated Solutions for Managing Incidental CAC Findings

Posted on by Partner Content

The rising prominence of coronary artery calcium as a prognostic marker for heart disease has created an emerging challenge for radiologists: how should they manage incidental CAC findings discovered on routine CT exams? Fortunately, new industry collaborations are making it possible to deliver CAC reports to clinicians without disrupting workflow. 

Routine CT scans are revealing data beyond their original diagnostic intent.

  • AI solutions – such as AVIEW CAC from Coreline Soft – play a pivotal role in identifying risks for cardiovascular disease, osteoporosis, and metabolic disorders – all from a single scan.

AI allows one CT scan to assess lung, cardiovascular, and skeletal health, improving diagnosis and treatment planning.

One imaging services provider that has put AVIEW CAC into use is 3DR Labs, which has been actively integrating the solution into its nationwide clinical network.

  • The partnership enables 3DR Labs radiologists to generate consistent, high-quality CAC reports directly within PACS, while significantly reducing turnaround times.

3DR Labs is finding that AVIEW CAC optimizes workflow efficiency and significantly reduces the time required for CAC assessment. 

  • It also ensures that radiologic technologists can perform quick QA checks, enhancing consistency and reliability in the delivery of the report.

The latest generation of the FDA-cleared AVIEW CAC features an upgraded user interface and advanced batch-scoring functionality. 

  • 3DR Labs is now working to expand AI-driven insights into lung and neuroimaging through Coreline’s broader AVIEW platform (AVIEW ILA for interstitial lung abnormalities and AVIEW BAS for brain CT).

Beyond diagnostic imaging, this collaboration supports growing demands for cost-efficiency in healthcare. 

  • As U.S. insurers and government agencies recognize the ROI potential of early AI detection, platforms like AVIEW CAC offer scalable, high-performance solutions that lower costs and streamline care delivery.

3DR Labs has also highlighted Coreline Soft’s role as a founding partner in AI Labs, the company’s vendor-neutral platform to deliver the latest AI innovations to radiology workflows.

The Takeaway

New partnerships like the collaboration between Coreline Soft and 3DR Labs are advancing the future of AI in radiology – focusing on automation, early detection, and better patient outcomes through powerful, clinically validated technologies. Such partnerships not only reflect increasing adoption of AI in U.S. healthcare but set the stage for global transformation in diagnostic imaging.

AI-Driven Diagnostics Detects Multiple Chest Diseases from Single CT Scan

Posted on by Partner Content

A new generation of AI solutions is enabling clinicians to detect multiple chest pathologies from a single CT scan. Lung cancer, cardiovascular disease, and chronic obstructive pulmonary disease (COPD) can all be detected in just one imaging session, ushering in a new era of more efficient imaging that benefits both providers and patients. 

Advances in CT lung cancer screening have been generating headlines as new research highlights the improved clinical outcomes possible when lung cancer is detected early. 

  • But lung cancer is just one of a “big three” of thoracic comorbidities – the others being cardiovascular disease and COPD – that can result from long-term exposure to toxic substances like tobacco smoke. 

These co-morbidities will be encountered more often as health systems ramp up lung cancer screening efforts, creating challenges for radiologists in managing the many incidental findings discovered with chest CT scans.

  • And it’s common knowledge that radiologists already have their hands full in an era of personnel shortages and rising imaging volumes. 

Fortunately, new AI technologies offer a solution. One of these is Coreline Soft’s AVIEW LCS Plus, an integrated three-in-one solution that enables simultaneous detection of lung cancer, cardiovascular disease, and COPD from a single chest CT scan. 

  • AVIEW LCS Plus is the only solution adopted for national lung cancer screening projects across key countries, including Korea (K-LUCAS), Germany (HANSE), Italy (RISP), and the pan-European consortium (4ITLR). 

Coreline’s solution is widely recognized as a pioneering AI platform for an era where unexpected findings can save lives, gaining increasing attention in academic journals and health policy reports alike.

  • In the U.S., AVIEW LCS Plus has been adopted by Temple Health, and the Pennsylvania system’s use of the solution in their Temple Healthy Chest initiative has been recognized as an innovative healthcare model within the Philadelphia region. 

Temple Health clinicians are finding that AI contributes to early detection of incidental findings, improved survival rates, and more proactive care planning.

  • AVIEW LCS Plus is streamlining lung cancer screening, helping identify chest conditions at earlier stages, when treatment is most effective. It is finding not only lung nodules but also undetected comorbidities that were often missed with conventional CT workflow. 

Coreline Soft will be presenting AVIEW LCS Plus in collaboration with Temple Health at the upcoming American Thoracic Society (ATS 2025) international conference in San Francisco from May 16-21. 

  • Attendees will be able to learn how AI in medical imaging can establish new standards, not just in diagnostics, but across policy, patient care, and hospital strategy. 

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.

Patients Want Mammo AI, But Mostly As Backup

Posted on by Brian Casey

Patients support the idea of having AI review their screening mammograms – under certain conditions. That’s according to a new study in Radiology: Imaging Cancer that could have implications for breast imagers seeking to integrate AI into their practices.

Mammography screening has been identified as one of the most promising use cases for AI, but clinical adoption has been sluggish for reasons that range from low reimbursement to concerns about data privacy, security, algorithm bias, and transparency. 

  • Vendors and providers are working on solving many of the problems impeding greater AI use, but patient preference is an often overlooked factor – even as some providers are beginning to offer AI review services for which patients pay out of pocket.

To gain more insight into what patients want, researchers from the University of Texas Southwestern Medical Center surveyed 518 women who got screening mammography over eight months in 2023, finding …

  • 71% preferred that AI be used as a second reader along with a radiologist.
  • Only 4.4% accepted standalone AI interpretation of their images.
  • 74% wanted patient consent before AI review.
  • If AI found an abnormality, 89% wanted a radiologist to review their case, versus 51% who wanted AI to review abnormal findings by radiologists.
  • If AI missed a finding, 58% believed “everyone” should be accountable, while 15% said they would hold the AI manufacturer accountable. 

Patient preference for use of AI in collaboration with radiologists tracks with other recent research. 

  • Patients seem to want humans to retain oversight of AI, and seem to value trust, empathy, and accountability in healthcare – values associated with providers, not machines. 

The findings should also be good news for imaging services companies offering out-of-pocket AI review services. 

The Takeaway

The new findings should be encouraging not only for breast imagers and AI developers, but also for the imaging services companies that are banking on patients to shell out their own money for AI review. As insurance reimbursement for AI languishes, this may be the only way to move mammography AI forward in the short term.

Getting Paid for AI – Will It Get Easier?

Posted on by Brian Casey

Reimbursement is one of the major stumbling blocks holding back wider clinical adoption of artificial intelligence. But new legislation was introduced into the U.S. Congress last week that could ease AI’s reimbursement path. 

For AI developers, getting an algorithm approved is just the first step toward commercial acceptance. 

  • Perhaps even more important than FDA clearance is Medicare reimbursement, as healthcare providers are reluctant to use a product they won’t get paid for. 

Reimbursement drives clinical AI adoption, as evidenced by a 2023 analysis listing the top algorithms by CPT claims submitted (Heartflow Analysis topped the list). 

  • But CMS uses a patchwork system governing reimbursement, from temporary codes like New Technology Add-On Payment codes that expire after 2-3 years to G-codes for procedures that don’t have CPT codes, on up to the holy grail of medical reimbursement: Category I codes. 

The new legislation – S.1399 or the Health Tech Investment Act – would simplify the situation by setting up a dedicated Medicare coverage pathway for AI-enabled medical devices approved by the FDA (called “algorithm-based healthcare services”), as follows … 

  • All FDA-approved products would be assigned a Category III New Technology Ambulatory Payment Classification in the HOPPS program.
  • NTAPC codes would last for five years to enable collection of cost data before a permanent payment code is assigned. 
  • Payment classifications will be based on the cost of service as estimated by the manufacturer. 

The bill at present has co-sponsors from both political parties, Sen. Mike Rounds (R-SD) and Sen. Martin Heinrich (D-NM). 

  • The legislation has also drawn support from industry heavyweights like GE HealthCare and Siemens Healthineers, as well as industry groups like AdvaMed and others.

The Takeaway

The new bill sounds like a great idea, but it’s easy to be skeptical about its prospects in today’s highly charged political environment – especially when even bipartisan compromises like the 2025 Medicare fix got scuttled. Still, S.1399’s introduction at least shows that the highest levels of the U.S. government are cognizant of the need to improve clinical AI reimbursement.

AI Helps Radiologists Read Prostate MRI

Posted on by Brian Casey

MRI is changing how prostate cancer is detected, diagnosed, and followed up. But even a technology as powerful as MRI could use a little help, as evidenced by a new study in Radiology showing that a commercially available AI algorithm could help radiologists diagnose clinically significant prostate cancer. 

Workup of suspicious prostate lesions is being reshaped by MRI in meaningful ways.

  • For example, MRI-guided biopsy is replacing systemic prostate biopsy without guidance, especially for patients with low to intermediate risk of prostate cancer. 

But prostate MRI isn’t perfect – yet. Radiologist performance can vary due to differences in experience, as well as variations in MRI acquisitions, tumor location, and cancer prevalence. Could AI help even out these variations? 

  • To find out, researchers from South Korea tested Siemens Healthineers’ syngo.via Prostate MR algorithm in 205 patients suspected of prostate cancer who were scheduled for biopsy based on clinical information (including previous MRI scans).

The AI algorithm’s performance was compared to that of experienced radiologists, and researchers also estimated its impact on radiologist interpretation if used as a reading aid, finding that for clinically significant prostate cancer… 

  • AI had lower sensitivity versus radiologists (80% vs. 93%).
  • But higher positive predictive value (58% vs. 48%).
  • Adding AI to radiologists’ interpretation more than doubled specificity (44% vs. 21%).
  • There were no cancer cases among lesions rated by both the algorithm and radiologists as not likely to be cancer (PI-RADS 1 or 2).

AI’s higher PPV indicates that it could help reduce unnecessary prostate biopsies, while also detecting clinically significant cancer that might have been missed by radiologists.  

The Takeaway

The new findings echo previous studies that demonstrate the value of AI for MRI of prostate cancer, but differ in that they investigate a commercially available algorithm – indicating that tools for better prostate MRI are becoming accessible to radiologists. 

Bridging Quality and Efficiency: Why Radiology Groups Are Adopting AI for Mammography Workflows

Posted on by Partner Content

By Dr. Roger Yang, President, University Radiology Group, and Mo Abdolell, CEO, Densitas

Radiology groups offering mammography services operate under ever-tightening demands, including MQSA EQUIP and ACR accreditation standards. Manual case selection, cumbersome paperwork, and lengthy review cycles often divert radiologists and technologists from what matters most – patient care.

But change is coming. By leveraging AI and mammography workflow automation, private radiology groups are reshaping how they manage quality, reduce administrative overhead, and advance patient care. 

AI-powered platforms can significantly streamline mammography quality management by:

  • Automating case selection for EQUIP reviews.
  • Measuring positioning metrics in near real-time.
  • Centralizing documentation to simplify compliance.

Some practices have reported up to a 90% reduction in EQUIP review time and 80% workload reduction in ACR accreditation using AI. But time savings are only part of the story.

Rather than waiting months for sporadic audits, technologists gain instant insights into positioning accuracy. This rapid feedback loop…

  • Accelerates targeted training.
  • Encourages continuous quality improvement.
  • Empowers technologists to self-monitor performance and identify gaps earlier. 

Today’s vendor-agnostic AI solutions integrate seamlessly with diverse imaging systems across multiple sites. 

  • Standards-based platforms can grow from a single mammography unit to dozens, helping radiology groups expand without adding complexity.

In a crowded marketplace, radiology practices that adopt AI-driven mammography quality management and automation stand out as forward-thinking leaders. Advantages include…

  • Enhancing patient perception: Offering efficient exams and high-quality imaging underscores a commitment to excellence, boosting satisfaction and referrals.
  • Leveraging analytics: Aggregated data on image quality and positioning helps leadership identify trends, optimize workflows, and highlight innovation.
  • Attracting top talent: Skilled technologists and radiologists gravitate toward practices with cutting-edge tools.

By integrating AI early, private practices can differentiate themselves, paving the way for growth and success.

Successful AI adoption and mammography workflow automation relies on more than just software. It requires:

  • Deep mammography expertise from vendors.
  • Robust training programs for staff.
  • Change training programs for staff.
  • Responsive customer support that fosters trust.

Mammography workflow automation cuts administrative burdens, curtails physician burnout, and speeds accreditation. Technologists receive clear, timely feedback, improving morale and performance. 

  • Meanwhile, patients benefit from streamlined workflows and consistent image quality, reinforcing trust in the practice.

The Takeaway

By embracing AI-driven mammography workflow automation and quality management, radiology groups can stay focused on delivering exceptional patient care while meeting regulatory requirements. This strategic investment propels private practices toward sustained growth and innovation, securing a competitive edge in a rapidly evolving healthcare landscape. Learn more.

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