Tag: radiologist

An All-in-One Radiology Platform Built for the AI Era

Posted on by Partner Content

Early in the COVID pandemic, software engineer Shiva Suri found himself working from home alongside his radiologist mother in his parents’ basement. What he saw would lead him to build New Lantern, an AI-native platform set to disrupt the legacy radiology software market.

Suri witnessed his “world-class radiologist” mom wasting far too much time switching between five different PACS platforms and repeating the same cumbersome reporting processes with each case.

“I thought a radiologist’s job was supposed to be playing Sherlock Holmes in images,” Suri recalls, “not constantly mouse-clicking all over their PACS and tab-dictating endlessly in their reporting software.”

That imperfect workflow is an unfortunate reality for today’s radiologists, who’ve seen their processes become more tedious, while their caseloads grow in both volume and complexity.

Rads Don’t Need Another Widget

Suri’s time spent working from home became the foundation for New Lantern’s bold mission:  keep radiologists’ eyes on their images and let AI do the rest. 

  • That mission evolved over time, as Suri’s first attempt at solving radiology’s efficiency problem was a widget to automate report impressions.
  • Radiologists loved it, but… each wave of praise came with requests for more automation, leading Suri to realize that radiology’s problems weren’t going to be solved with another widget. The solution had to be fundamentally different.

The Time Is Right for an All-in-One Solution

Developing radiology’s go-to reading and reporting platform had to start with radiologists’ dream state, with their eyes on the viewer, reading image after image. 

  • It had to be based on the understanding that this dream can’t be achieved while radiologists are navigating a loosely integrated software stack.
  • The good news is, now is the perfect time to solve radiology’s software problem. The radiologist shortage and surging imaging volumes are finally driving radiology practices to look for new tech partners, and the emergence of generative AI is allowing startups to gain traction in segments that have long been dominated by entrenched legacy players. 

Enter New Lantern Curie

This perfectly timed mix of tech and market readiness set the stage for Curie, New Lantern’s all-in-one platform that combines a smart worklist, cloud PACS viewer, and AI reporter to produce AI-automated radiology report drafts.

Radiology report automation is no small task, and there’s a lot that goes into Curie’s ability to automate over 75% of non-diagnostic radiology work…

  • Streamlined Dictation – Radiologists free-dictate positive findings (no punctuation or commands), and the AI weaves them into complete sentences, generates guideline-based impressions (calculating BI-RADS, etc.), and flags errors.
  • No Tech Translations – Curie uses OCR technology to decipher technologist worksheets, applies clinical context via an LLM, and intelligently places data in the right report sections.
  • Remove Repetition – Radiologists no longer need to dictate measurements or enter prior dates. Curie handles these and a long list of other duplicative tasks for them.

The Numbers Tell the Story

All of these automations really add up, giving radiologists over 100 minutes back per shift, so they can get more done and get their lives back.

Here’s one real-world example presented at SIIM 2025 of a radiologist’s process for reading a pulmonary embolism CTA chest exam, before and after Curie…

  • Words dictated — 205 vs. 57
  • Punctuation marks & commands — 19 vs. 0
  • Fields navigated — 32 vs. 1
  • Metadata entries — 8 vs. 0 

In this example, Curie produced the same complete, accurate report with 72% fewer dictated words and 97% less navigation through dictation fields and hanging protocol changes. That’s one type of “AI taking radiologists’ jobs” that just about every radiologist would welcome.

The Takeaway

As imaging volumes surge and antiquated platforms push radiologists to the breaking point, New Lantern Curie offers them a way to work like it’s 2025 instead of 2005 – automating the fragmentation and duplication out of their days so world-class radiologists like Shiva Suri’s mom can focus on what they do best: reading images.

Learn more about New Lantern and its all-in-one approach to radiology workflow in this Imaging Wire Show video interview. 

ECR 2025 Video Highlights

Posted on by Brian Casey

The theme of ECR 2025 was Planet Radiology, and those who were in attendance at Austria Center Vienna last week witnessed the role that radiology can play in reducing medical imaging’s contribution to climate change.

As always, ECR 2025 was the focus of cutting-edge research in AI, as well as the latest findings in traditional applications like cancer screening.

In this special edition of The Imaging Wire newsletter, we offer a recap of our ECR 2025 videos with thought leaders and imaging vendors from the exhibit floor. 

We hope you enjoy watching our ECR 2025 videos as much as we enjoyed producing them! 

Check out the ECR 2025 videos below or visit the Shows page on our website, and keep an eye out for our next Imaging Wire newsletter on Thursday.

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.

Using AI-Powered Automation to Help Solve Today’s Radiology Crisis

Posted on by Partner Content

Reimbursement cuts. Radiologist and staff shortages. Rising costs. Surging imaging volumes. Overwhelming staff workloads. Shrinking margins. 

Sound familiar?

Radiology departments, imaging centers, and radiology practices are facing a perfect storm of challenges to deliver high-quality patient care while remaining profitable and competitive. 

  • This familiar narrative emphasizes the need for change and to embrace automation, AI, and technology solutions that automate routine tasks. 

RADIN Health has developed an innovative, cloud-based (SaaS), all-in-one technology stack based on the firsthand experience of radiologist Alejandro Bugnone, MD, CEO and medical director of Total Medical Imaging (TMI), a teleradiology group that reads for imaging centers and hospital systems nationally.  

  • Dr. Bugnone and his team of radiologists were similarly suffering from supply and demand imbalance, reimbursement cuts, increasing study volumes, and customer pressures to maintain their margin. 

As a software developer and seasoned radiologist, Dr. Bugnone was equally frustrated by the lack of a comprehensive, end-to-end technology solution in the market to address these same issues for his teleradiology practice.  

  • In evaluating numerous RIS, PACS, AI voice recognition, and workflow management solutions, his team found that each required expensive interfaces, separate company fees, and ongoing support, yet as an ecosystem still did not deliver a seamless experience that would provide a return on investment. 

An alternative is a system based on straight-through processing, a concept first pioneered in the financial services industry in which automation electronically processes transactions without manual intervention. 

“I knew there had to be a better way forward. I founded RADIN Health for healthcare and teleradiology practices [like TMI], imaging centers, and radiology departments based on straight-through processing, similar to how Wall Street sped up financial transactions without any human intervention,” Dr. Bugnone said. 

RADIN Health is a cloud-based platform that combines RIS, PACS, dictation AI, and workflow management into an all-in-one software solution. 

  • It leverages artificial intelligence, machine learning, OCR/AI, natural language processing (NLP), and other intellectual property.

Dr. Bugnone said TMI has achieved remarkable efficiencies with RADIN. 

“Our results at TMI have been staggering since implementing RADIN over the past 18 months for our complex teleradiology practice,” Dr. Bugnone noted. “With RADIN DICTATION AI, our radiologists have increased their productivity and efficiency, reducing dictation times 30% to 50%.” 

By adding RADIN SELECT, TMI reduced its SLAs more than 50% and FTEs by 70% for managing operational workflow tasks, all while adding 35% in study volumes.  

  • RADIN’s all-in-one technology solution has enabled Total Medical Imaging to meet the challenges of the radiology crisis without hiring new personnel – simply by unlocking the efficiency of their existing staff. 

“We have enjoyed significant growth in 2024 without the need to hire additional staff,” Dr. Bugnone concluded.

Watch the video below to see how RADIN’s all-in-one solution can help your practice.

Optimizing Front Office Operations through Integrated Apps and Cloud-Based RIS/PACS

Posted on by Partner Content

Paradox of High Patient Volumes

At first glance, it may appear having more patients should naturally lead to higher revenue. When you consider extra labor costs and the fact that reimbursements are decreasing, increased volume can turn into diminishing returns.

  • Basically, the cost of adding more staff can end up being higher than the value of additional patient volumes.

Optimal management of growing patient volumes requires a new way of working with automation and cloud-based apps that replace the heavy burden of manual processes.

  • By using technology to eliminate processes, medical facilities manage patient loads better without the need for more labor costs. 

This proactive approach not only improves efficiencies but also lets front office staff focus on patient needs instead of getting bogged down with administrative tasks. 

  • Ultimately, shifting towards automation and consolidation of tasks is key to maintaining clinic profitability and keeping high standards of care, especially with increasing medical demands.

How RamSoft Can Help Simplify Front Office Operations 

Achieving workflow excellence starts with a single sign-on into a unified RIS/PACS and providing access to complementary medical imaging apps via a single worklist in the cloud. 

  • By leveraging cloud applications with scalability across facilities, organizations can “build as they grow,” while maintaining control and flexibility.

RamSoft PowerServer and OmegaAI RIS/PACS platforms reduce administrative burdens and costs associated with manual processes. Here’s how…

  • Blume – Patient Portal: Patient access to diagnostic images and reports, imaging sharing with referring clinicians and family, self-scheduling, intake forms, and appointment notifications. These self-service features decrease the number of phone calls, the time needed for patient registration, and the manual process of intake form completion and filing. 
  • pVerify: Batch verification and real-time eligibility (authorization available soon) eliminates the need to call multiple insurance providers, freeing up staff time while reducing denials. 
  • PracticeSuite: An embedded solution including workflow options to accommodate entries from the RIS/PACS worklist or within the billing module. Quickly accesses top billing functions, Payment Ledger for balances and eligibility, and Payment Entry to add payment and print a receipt. 
  • openDoctor: Automated appointment notifications through SMS and email which replaces lists of confirmation calls and reduces missed appointments. 
  • InterFAX by Upland: Integrated digital workflow for inbound (available soon) and outbound faxes, reducing the need for manual acceptance and processing of referral or report faxes. 

Mobile Applications Are Building a Patient-Centric Experience

Protecting patient data is business-critical for all medical practices, as it is for RamSoft. We’re using Microsoft Azure Cloud to ensure all data and applications are secure.

  • Workflow optimization in medical imaging can significantly impact the patient experience, leading to increased loyalty and satisfaction. 

Is Your Practice Operating Optimally?

Explore how RamSoft’s new automation applications, including patient engagement tools, integrated with cloud-based RIS/PACS can improve operations and profitability of your practice. 

Learn more on the company’s website or book a demo at RSNA 2024 for booth #6513 in the North Hall.  

Patients Unclear on Imaging Costs

Posted on by Brian Casey

A new study in Health Policy and Technology shows that patients are surprisingly unclear on how much their imaging exams will cost them. Researchers found that few knew their imaging facilities had price estimator tools and even fewer were aware of their out-of-pocket estimates.

The U.S. government has been trying to make healthcare more transparent and understandable for patients through a variety of new rules it’s implemented in recent years, such as “information blocking” rules that prevent providers from withholding patient data.

  • In 2021, CMS required health systems to notify patients of out-of-pocket expenses and make available tools for estimating prices. 

But how knowledgeable are patients about these initiatives? 

  • Researchers from UC Irvine and the University of Michigan surveyed 423 patients scheduled for CT, PET/CT, or MRI scans in Southern California to find out how much they knew about their out-of-pocket costs. 

Researchers discovered that …

  • Only 11% of patients were aware of their out-of-pocket estimates before getting their scans.
  • Only 17% knew their imaging facilities had price estimator tools.
  • 53% said their illness has been a financial hardship, but only 34% were worried about their out-of-pocket costs for imaging.
  • No patient used the hospital’s estimator tool.
  • Patients were less likely to know their out-of-pocket costs if they had lower income (<$50,000), more financial hardship, and no comorbidities. 

The results show that, two years after out-of-pocket transparency rules went into effect, patients are still unclear on their imaging costs. 

  • This is a major problem due to the high variation in imaging prices that’s been documented in other studies, such as 2023 research that found MRI scans ranging in price from $878 to $3,403.

More outreach could help patients better understand costs. 

  • Such outreach could be made through automated calls or even messages through patient portals prior to their exams.

The Takeaway
The new study – when coupled with recent research on patient reports – shows that radiology still has a ways to go when it comes to keeping patients informed about their imaging exams. Getting patients more involved not only will have economic benefits, but could also help patients participate in their own care.

Better Prostate MRI with AI

Posted on by Brian Casey

A homegrown AI algorithm was able to detect clinically significant prostate cancer on MRI scans with the same accuracy as experienced radiologists. In a new study in Radiology, researchers say the algorithm could improve radiologists’ ability to detect prostate cancer on MRI, with fewer false positives.

In past issues of The Imaging Wire, we’ve discussed the need to improve on existing tools like PSA tests to make prostate cancer screening more precise with fewer false positives and less need for patient work-up.

  • Adding MRI to prostate screening protocols is a step forward, but MRI is an expensive technology that requires experienced radiologists to interpret.

Could AI help? In the new study, researchers tested a deep learning algorithm developed at the Mayo Clinic to detect clinically significant prostate cancer on multiparametric (mpMRI) scans.

  • In an interesting wrinkle, the Mayo algorithm does not indicate tumor location, so a second algorithm – called Grad-CAM – was employed to localize tumors.

The Mayo algorithm was trained on a population of 5k patients with a cancer prevalence similar to a screening population, then tested in an external test set of 204 patients, finding …

  • No statistically significant difference in performance between the Mayo algorithm and radiologists based on AUC (0.86 vs. 0.84, p=0.68)
  • The highest AUC was with the combination of AI and radiologists (0.89, p<0.001)
  • The Grad-CAM algorithm was accurate in localizing 56 of 58 true-positive exams

An editorial noted that the study employed the Mayo algorithm on multiparametric MRI exams.

  • Prostate cancer imaging is moving from mpMRI toward biparametric MRI (bpMRI) due to its faster scan times and lack of contrast, and if validated on bpMRI, AI’s impact could be even more dramatic.

The Takeaway
The current study illustrates the exciting developments underway to make prostate imaging more accurate and easier to perform. They also support the technology evolution that could one day make prostate cancer screening a more widely accepted test.

Indies Surge in Imaging IT

Posted on by Brian Casey

The market for medical imaging IT technology continues to shift, with a pair of surging independent players growing rapidly in a sector that’s long been dominated by multinational OEMs. That’s according to the latest report on the imaging IT market by UK market intelligence firm Signify Research. 

The new report is projecting that the global market for imaging information technology will grow 18% over the next few years, from $5.6B in 2023 to $6.6B in 2028. 

  • Radiology will continue to dominate with a majority of sales, with cardiology IT a distant – but growing – second. Advanced visualization and operational workflow tools will make up the rest.

In terms of vendors, the top three market leaders of 2023 were GE HealthCare, Philips, and Fujifilm, but more recently, Visage Imaging and Sectra have been gaining market share. 

  • The report echoes recent news that has seen some of the largest multi-site enterprise imaging installations going to Visage and Sectra; a recent KLAS Research report also showed both companies’ growing momentum. 

Some of the other major points from the report include … 

  • Major growth in cloud deployment will occur – by 2028, 37% of the global imaging IT market will be in either hybrid or fully hosted environments
  • Cloud will represent 44% of the total radiology IT market by 2028
  • On a regional basis, the Middle East will see “significant growth” in imaging IT from 2024 to 2026, particularly in the Gulf States
  • Recovery is expected in China and the ASEAN nations, while India’s growing economy is driving healthcare digitization
  • Latin America is showing rising interest in AI and cloud technologies, but national elections could complicate matters

The Takeaway
The new Signify Research report underscores the evolving nature of the imaging IT market, as independent vendors rise to challenge multinational OEMs that dominated the sector for years. Be sure to check out Signify’s helpful infographic on LinkedIn that succinctly wraps up the changes.

AI Detects Incidental PE

Posted on by Brian Casey

In one of the most famous quotes about radiology and artificial intelligence, Curtis Langlotz, MD, PhD, once said that AI will not replace radiologists, but radiologists with AI will replace those without it. A new study in AJR illustrates his point, showing that radiologists using a commercially available AI algorithm had higher rates of detecting incidental pulmonary embolism on CT scans. 

AI is being applied to many clinical use cases in radiology, but one of the more promising is for detecting and triaging emergent conditions that might have escaped the radiologist’s attention on initial interpretations.

  • Pulmonary embolism is one such condition. PE can be life-threatening and occurs in 1.3-2.6% of routine contrast-enhanced CT exams, but radiologist miss rates range from 10-75% depending on patient population.

AI can help by automatically analyzing CT scans and alerting radiologists to PEs when they can be treated quickly; the FDA has authorized several algorithms for this clinical use. 

  • In the new paper, researchers conducted a prospective real-world study of Aidoc’s BriefCase for iPE Triage at the University of Alabama at Birmingham. 

Researchers tracked rates of PE detection in 4.3k patients before and after AI implementation in 2021, finding … 

  • Radiologists saw their sensitivity for PE detection go up after AI implementation (80% vs. 96%) 
  • Specificity was unchanged (99.1% vs. 99.9%, p=0.58)
  • The PE incidence rate went up (1.4% vs. 1.6%)
  • There was no statistically significant difference in report turnaround time before and after AI (65 vs. 78 minutes, p=0.26)

The study echoes findings from 2023, when researchers from UT Southwestern also used the Aidoc algorithm for PE detection, in that case finding that AI cut times for report turnaround and patient waits. 

The Takeaway

While studies showing AI’s value to radiologists are commonplace, many of them are performed under controlled conditions that don’t translate to the real world. The current study is significant because it shows that with AI, radiologists can achieve near-perfect detection of a potentially life-threatening condition without a negative impact on workflow.

Lung Screening Narrows Disparities

Posted on by Brian Casey

New research confirms that not only does low-dose CT screening reduce lung cancer mortality, it can also narrow health disparities. Researchers found that screening’s beneficial impact was greater at lower socioeconomic levels in a new study published in Lancet Regional Health – Europe.

As we mentioned in our last issue, CT lung cancer screening is gaining momentum globally; at the same time, researchers have documented greater mortality and morbidity for a variety of diseases among racial minorities and at lower socioeconomic levels.

  • This difference can be especially profound when it comes to lung disease, given higher smoking rates among some minority groups and economically disadvantaged populations.

In the original UK Lung Cancer Screening Trial (UKLS) in 2021, researchers found that a single CT screening round produced a 16% lung cancer mortality reduction. 

  • The new study is a secondary analysis of UKLS to investigate whether CT lung screening’s impact differed by socioeconomic status, which is important given that smoking occurs in England at higher rates in the most deprived neighborhoods compared to wealthier ones (24% vs. 6.8%).

UKLS researchers compared lung cancer mortality rates in 4k individuals in different groups classified by a widely used socioeconomic barometer. They found that … 

  • CT lung screening had the same lung cancer mortality benefit in both low and high socioeconomic groups (-19% vs. -20%)
  • But there was a bigger reduction in death from COPD in lower socioeconomic groups (-34% vs. +4%)
  • And fewer deaths from other lung diseases (-32% vs. +10%)
  • While cardiovascular mortality was also lower (-30% vs. -13%)
  • All-cause mortality was lower in lower socioeconomic groups – a benefit not seen at higher levels

Lung screening’s reduction in all-cause mortality is particularly intriguing, as this is an accomplishment that has eluded most other cancer screening tests – a point that has been repeatedly hammered home by screening skeptics.

The Takeaway

The new findings highlight how – to a greater degree than other major cancer screening tests – CT lung screening has the potential to address ongoing racial and socioeconomic healthcare disparities. It’s yet another reason to press for broader adoption of lung screening.

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