Author: Partner Content

AI in Radiology: Old Problems, New Tech

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Radiology has seen this movie before. Big promises (efficiency, accuracy, burnout relief). Big anxieties (ROI, workflow chaos, pressure to “keep up”). The question isn’t whether AI is powerful. It’s whether we’ve learned how to deploy new technology without repeating the pain of PACS migrations and the EHR era.

The Myth of the Perfect Rollout. Health technology assessment (HTA) sounds great in theory – rigorous, comprehensive, evidence-first. In practice, few organizations have the time, talent, or budget to execute it at scale. 

  • Remember EHRs: adoption happened because policy and money forced it, not because the playbook was tidy. Healthcare’s default pattern is to adopt, then evolve – messy, market-driven, and iterative. Waiting for perfect plans is how you get left behind.

Are AI’s Problems really new?

  • Black box déjà vu. Radiology has long trusted complex, opaque systems (reconstruction algorithms, vendor-specific pipelines). What mattered – and still matters – is validated performance and dependable outputs, not full internal transparency.
  • Model drift ≈ old friends. We’ve always recalibrated clinical tools as populations and scanners change. Monitoring and revalidation are known problems, not alien ones.

What’s Different This Time? Unlike the top-down EHR mandate, AI is largely market-driven. That gives providers agency. 

  • AI solutions must save time, improve outcomes, or avoid costs – not just publish a ROC curve. They must show operational value inside the native radiology workflow.

Fortunately, there are ways to adopt AI and then evolve your processes to make it work…

  • Workflow or bust. Demand in-viewer evidence objects, one-click report insertion, and EHR write-back. If AI adds steps, it subtracts value.
  • Start narrow, scale deliberately. Pick high-volume, high-friction tasks. Prove value in weeks, not years. Expand only when the operational signal is undeniable.
  • Measure what matters. Track operational metrics like seconds saved and coverage (e.g. eligible cases processed before dictation), reliability (e.g. results present before finalization, fail-open behavior), and user friction like context-switching rate and time-to-evidence.
  • Monitor. Stand up organization and site-level performance checks. Treat AI like equipment – scheduled, observed, and maintained.
  • Invest in long-term value. Favor standards, vendor-agnostic interoperability, clear telemetry, and transparent pricing.

The Takeaway

AI’s success in radiology won’t be defined by elegance of algorithms but by pragmatism of deployment. This will be an evolution – hands-on, incremental, sometimes messy. The difference now is that radiology can drive. Make the technology serve the service line – not the other way around.

Target the toughest workflows. Adapt and evolve with Densitas Breast Imaging AI Suite.

AI First Drafts: A New Dawn for Radiology Reporting

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For radiologists – the medical detectives who find clues in our medical images – the daily grind can feel like a “death by a thousand cuts.” Much of their time is spent not on diagnosis, but on tedious reporting. 

Now, a new generation of artificial intelligence is stepping in to serve as a high-tech scribe, automating the drudgery.

  • This AI tackles reporting, the most time-consuming part of radiologists’ workflow.

AI-enabled radiology reporting makes transcribing data from technologist worksheets a thing of the past, using Optical Character Recognition (OCR) to decipher everything, even what looks like “chicken scratch handwriting.” Then…

  • A large language model (LLM) applies clinical context to ensure it understands the meaning.
  • It intelligently injects that data into the correct sections of the radiologist’s personal report template.
  • Finally, it performs its own “inference,” like calculating a TI-RADS score and dropping it right into the impression.

Modern AI also learns from a radiologist’s actions, providing a hands-free way to build a report, with features such as…

Smart Measurements: When a lesion is measured, the AI recognizes the location and automatically adds the data and comparisons to prior scans into the report.

Automated Prior Population: Instead of struggling with speech-to-text, the AI notices when a prior study is opened for comparison and automatically populates that exam’s date.

Streamlined Expert Findings: A radiologist can simply state positive findings, and the AI acts as both writer and editor. 

AI-enabled radiology reporting weaves dictated phrases into complete sentences, generates an impression based on clinical guidelines like BI-RADS, and serves as a vigilant proofreader, flagging errors like laterality mistakes or semantic impossibilities. 

As AI technology matures, the software itself is becoming easier to build. The true differentiator is the team behind it. 

  • For radiologists evaluating these new reporting tools, it’s critical to look for teams that are “AI native” – built from the ground up with AI at their core. 

Companies founded on these principles, such as New Lantern, are pioneering these all-in-one radiology reporting solutions, treating the challenge not as a problem to be fixed with another widget, but as an opportunity to build one complete, intelligent platform. 

The Takeaway 

The evolution in AI-enabled radiology reporting isn’t about replacing radiologists; it’s a tool to augment their skills. Radiologists who harness AI to create reports faster will significantly outpace those who do not, allowing them to return their full focus to the art of diagnosis.

Why Radiology Leaders Are Turning to AI – And Why They’re Not Looking Back

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From single-scanner clinics to university hospitals, radiology leaders around the globe face the same challenge: keeping up with rising patient demand while managing costs.

MRI volumes are climbing. Scanner hours and budgets? Not so much.

  • Under pressure to do more with less, decision-makers are reaching a conclusion that was unthinkable just a few years ago: AI-powered MRI is no longer a novelty – it’s a necessity.

No matter the size or scale of the operation, diagnostic imaging providers face a familiar set of challenges:

  • High capital costs – New scanners cost seven figures, and upgrades run hundreds of thousands.
  • Limited capacity – Most sites can’t easily add scanners, staff, or hours to meet demand.
  • Rising demand – MRI volume continues to grow as chronic conditions rise and preventive care gains traction.
  • Patient expectations – Long, uncomfortable exams frustrate patients who may look elsewhere.

AI offers a path forward, helping imaging teams handle more studies without compromising diagnostic standards.

AIRS Medical built SwiftMR, AI-powered MRI reconstruction software, to meet today’s imaging challenges. Hospitals and clinics in over 35 countries use SwiftMR to:

  • Reduce scan times by up to 50% compared to standard protocols.
  • Deliver sharper images radiologists can trust.
  • Enhance the patient experience with shorter exams and fewer motion-related rescans.

SwiftMR is vendor-neutral, compatible with all MRI makes, models, and field strengths.

FDA-cleared, MDR-certified, and clinically validated, SwiftMR is trusted by over 300 imaging providers in the U.S. and over 1,000 globally, including:

These outcomes show that AI-powered MRI delivers tangible operational, clinical, and financial benefits across site types and geographies. 

Watch this video to learn more about SwiftMR.

The Takeaway

Radiology leaders are relying on SwiftMR to transform how they deliver care. From enterprise networks to single-scanner clinics, imaging teams are unlocking new levels of efficiency and patient care.

AI-Driven Lung Cancer Screening and Improving Patient Outcomes

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AI is reshaping clinical decision-making, optimizing resource allocation, and enhancing both patient outcomes and experience in CT lung cancer screening. Radiology providers are successfully integrating new AI software tools into hospital operations – supporting diagnostic accuracy and improving patient outcomes.

At the center of this trend is Coreline Soft’s FDA-cleared AVIEW LCS Plus, a 3-in-1 solution capable of detecting lung nodules, quantifying emphysema, and analyzing coronary artery calcification – all from a single low-dose CT scan. 

  • AVIEW LCS Plus is in use at Temple Health, a nationally recognized institution in the U.S. Northeast, where it has allowed providers to streamline clinical workflows from detection to follow-up, delivering measurable improvements in care and ROI.

Coreline Soft will co-host a strategic webinar with the Temple Lung Center on August 1 at 1:30 PM ET, focused on AI-powered lung cancer screening and the evolving paradigm of early detection for chest diseases.

The webinar will offer firsthand insight into how Temple Health is drawing attention as a model for integrating AI beyond diagnosis – transforming it into a scalable, patient-centered care strategy.

The discussion will focus on two main areas…

  • Real-world outcomes: How AI improved diagnostic efficiency, early detection, and comorbidity detection.
  • A deep dive into the precision technology of the AVIEW LCS Plus platform.

AI like Coreline’s is not replacing clinical judgment, but reinforcing it, enhancing radiologists’ ability to detect, triage, and treat lung disease earlier and more efficiently, Criner believes. 

  • The webinar is open to pulmonologists, radiologists, cardiologists, respiratory-adjacent professionals, hospital stakeholders and administrators, and primary care providers across the U.S. and Canada. Interested participants can register for free in advance via the official registration link. 

The Takeaway

AI solutions like Coreline Soft’s AVIEW LCS Plus platform are having a real-world impact on healthcare providers as they roll out CT lung cancer screening programs. Sign up to learn more on August 1.

Molecular MRI Adds Certainty to Cancer Diagnosis

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MRI has become an important tool in the detection, diagnosis, and treatment planning for many cancers, especially solid tumors. However, up until now, a lack of specificity has held back the full potential of MRI.  

While MRI is very good at identifying areas of interest, factors such as infection, benign tumors, post-traumatic areas, and inflammation can all increase vascularity and, therefore, enhancement of contrast and signal changes.  

  • As a result, MRI has a high rate of false positives – findings that may be flagged as something of concern but that are not necessarily malignant lesions.  

This lack of accuracy results in clinical care teams performing too many confirmatory biopsies, with most being benign.

Now a novel class of molecular imaging contrast agents developed by Imagion Biosystems brings a new level of specificity to MRI. 

  • The company’s MagSense imaging agents have the potential to improve the clinical utility of the large installed base of MRI systems across the globe through improved accuracy of interpretation, avoiding biopsies of benign lesions, driving earlier intervention and improving outcomes and quality of life.

Unlike gadolinium-based agents that non-specifically enhance tissue vascularity regardless of cause, MagSense imaging agents target receptors on cancer cells.  

  • By combining magnetic nanoparticles that have high susceptibility and r2 relaxivity with cancer-specific biomarkers, molecular MRI becomes possible.

Imagion’s superparamagnetic iron oxide nanoparticles are coated with a cancer-specific targeting moiety, such as an antibody or peptide.

  • The cancer biomarker molecule causes the particles to bind to target-specific cancer cells, if present. If the lesion in question is not the target cancer, the particles do not bind.

Where the imaging agent has become attached to the tissue, the nanoparticles produce an identifiable change in MRI signal. 

  • This signal is easily detected by radiological review and can be quantitatively assessed.

Imagion has developed cancer-specific contrast imaging agents for HER2 breast cancer, prostate cancer, and ovarian cancer, and the MagSense platform can be adapted for any type of cancer for which there is a targeting moiety.  

  • Imagion is now preparing to initiate a multisite phase 2 study in the U.S. in HER2+ breast cancer patients to optimize imaging parameters and compare MagSense imaging to the standard of care.  

The Takeaway

Molecular-specific imaging agents like the MagSense technology from Imagion Biosystems create the opportunity for molecular MRI to fundamentally change how radiologists detect and monitor cancers. 

The company is publicly traded (ASX:IBX) and is looking to expand its U.S. investor base as it advances through its clinical programs. To become involved as an investigator or investor or to learn more visit their website.

Integrated Solutions for Managing Incidental CAC Findings

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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

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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. 

High-Risk Breast Clinics: A Smart Move for Imaging Providers

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High-risk breast cancer clinics are no longer just a good idea – they’re becoming a strategic imperative. These programs, focused on identifying and managing women at elevated risk for breast cancer, are proving their value clinically and financially.

For imaging providers, they present an opportunity both to improve care and grow service lines in a value-based care environment, while also differentiating themselves in increasingly competitive markets. A recently published white paper offers a full explanation of the benefits of high-risk breast clinics.

Treating late-stage breast cancer is extremely costly – $76,000+ in the final year of life alone – and it represents a major portion of oncology spend nationwide. 

  • By identifying high-risk patients early and offering enhanced surveillance with breast MRI, clinics can diagnose more cancers at early stages, when treatment is more effective and less expensive. 

Studies show MRI screening in BRCA1 carriers is cost-effective at ~$50,900 per QALY. 

  • This makes it a smart investment from both a patient and payor perspective.

Historically, preventive programs were considered cost centers. Not so with high-risk breast clinics. 

  • Once a patient is flagged as high risk, the care pathway includes reimbursable   genetic counseling and testing, supplemental imaging (MRI or contrast-enhanced mammography), biopsies, chemoprevention, and even risk-reducing surgeries. Each step creates downstream revenue for imaging centers and affiliated specialists – all while improving patient care.

Integration is key. Embedding risk assessment tools like Tyrer-Cuzick or AI-based models (e.g. Mirai) into the high-risk clinic’s imaging workflow enables automatic triage. 

  • Patients with ≥20% lifetime risk can be directly referred to the high-risk clinic. Some models now use short-term risk from imaging data alone to identify the top 5-10% women most likely to develop cancer within five years – significantly outperforming traditional tools in clinical studies.

Successful clinics rely on multidisciplinary teams. Advanced-practice providers manage most visits. Genetic counselors – in person or via telehealth – manage testing results and family history. Patient navigators coordinate follow-ups and authorizations. 

  • This team-based approach keeps physician time focused and costs under control, ensuring the clinic operates efficiently and sustainably.

The Takeaway

For imaging providers, high-risk breast clinics offer a powerful blend of patient impact and business growth. They reduce expensive late-stage cancers, drive high-value imaging, and create long-term patient relationships. In an era of value-based care, they’re not just a clinical upgrade – they’re a strategic advantage. Forward-thinking imaging leaders are recognizing this model as essential to the future of preventive breast care.

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

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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.

Unlocking Body Composition Insights with Voronoi Health Analytics

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Body composition plays a pivotal role in monitoring organ and tissue health and predicting treatment outcomes. Accurate changes in body composition metrics can indicate reduced muscle quantity and quality – a sign of sarcopenia – as well as altered fat distribution in organs such as the liver in metabolic diseases, epicardial and paracardial fats in cardiovascular health, and more.

However, manual segmentation is time-consuming and labor-intensive. 

  • Voronoi Health Analytics eliminates this bottleneck by combining cutting-edge AI with efficient visualization tools, automating the extraction of body composition metrics from CT and MRI scans. The company’s solutions transform imaging data into actionable insights, improving patient outcomes.

Voronoi Health Analytics provides innovative, intuitive AI tools that enable clinicians and researchers to extract quantitative body composition measurements rapidly and with high accuracy – no programming required. 

  • The company’s platforms are trusted by over 175 research labs across 25 countries, with numerous publications validating their accuracy and impact on clinical care and medical research.

Voronoi has two flagship solutions …

  • DAFS: A comprehensive 3D segmentation platform for analyzing multiple tissues, organs, lesions, and vasculature across CT and PET/CT imaging. DAFS also overlays CT segmentations onto PET scans, enabling rapid, high-accuracy assessments of PET tracer uptake in organs, tissues, and lesions.
  • DAFS Express: Optimized for single-slice body composition analysis from CT and MRI scans, this tool delivers precise measurements of skeletal muscle, visceral fat, intermuscular fat, and subcutaneous fat in seconds, making it ideal for high-throughput clinical settings.

Accurate body composition analysis is critical for staging body habitus, detecting onset of signatures of adverse health such as metabolic or cardiovascular disorders, evaluating disease progression, and monitoring organ and tissue health as a function of disease and intervention. Voronoi’s platforms address key challenges such as …

  • Reducing Workloads: Automate routine segmentation tasks and allow clinicians to focus on complex cases.
  • Improving Precision: Deliver consistent, reproducible results across patients and studies.
  • Advancing Care: Provide predictive insights that help optimize treatment strategies.

DAFS and DAFS Express seamlessly integrate into existing imaging workflows, enhancing efficiency without disrupting operations.

Body composition analysis goes beyond measuring muscle and fat. It quantifies all organs and tissues, creating data that drives predictive models. 

  • Voronoi’s vision is to empower healthcare professionals with tools that simplify complexity, support proactive care, and enhance patient outcomes.

Discover how Voronoi Health Analytics is revolutionizing body composition analysis. Visit the company’s website to request a demo and elevate your workflow today.

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