Tag: radiologists

Early News from ECR 2026

Posted on by Brian Casey

ECR 2026 opened yesterday with a light-filled opening ceremony that beautifully illustrated this year’s theme, “Rays of Knowledge.” The ceremony was conducted before an overflow audience in the Austria Center Vienna, with ECR 2026 President Prof. Minerva Becker proudly presiding over a mix of speeches, honorary awards, and musical performances for which ECR is famous.

ECR 2026 is taking place as European radiology reaches an inflection point. The region has workforce shortages that are as serious (if not more so) than the U.S., and it must also accommodate rising demand for medical imaging. 

As in the U.S., artificial intelligence is being held up as a potential solution to enable radiologists to do more with less. 

  • In some ways, Europe is ahead of the U.S., having conducted large-scale prospective trials like MASAI to test AI’s value for tasks like breast screening. One country – Italy – has even recommended that AI be used routinely for breast screening.  

But from a regulatory standpoint, skepticism toward AI may be even stronger in Europe than in the U.S. 

  • The European Union in 2024 implemented the AI Act to promote human-centered and trustworthy AI, and the act classifies AI algorithms as among the highest-risk devices. Some industry observers believe the act may already be slowing the introduction of new algorithms into the European market, even as the U.S. is removing regulatory guardrails on clinical AI.

Be that as it may, the ECR continues to reinforce its reputation as a forum for top-notch clinical content, and the first day of sessions did not disappoint. In particular, AI for lung cancer screening was a prominent focus, including the following sessions…

  • Harrison.ai’s chest CT AI algorithm turned in 91% sensitivity for detecting pulmonary nodules in 1.1k LDCT scans, with an average of 1.12 false positives per case.
  • Results from the RELIVE study of lung cancer screening in France showed that AI from Median Technologies boosted AUC for radiologists (0.843 vs. 0.828), with less experienced radiologists seeing a 4.8% AUC improvement. 
  • A survey of patients and clinicians in Northern Ireland found both groups were positive about using AI for lung cancer to reduce waiting times, but neither group liked the idea of autonomous AI.
  • The LUNA25 challenge tested AI algorithms developed by five teams for estimating malignancy risk of lung nodules, finding that the top AI had standalone AUC performance better than the average of 75 radiologists (0.78 vs. 0.69).
  • Dutch researchers tested four commercially available AI algorithms for LDCT lung screening, finding wide variation in sensitivity (77% to 92%).
  • Carebot’s AI CXR software was used to analyze 96.5k chest X-rays from nine Czech hospitals over six months, finding 54 previously undiagnosed thoracic cancers.

The Takeaway

ECR 2026 continues through Sunday, and we’ll be on hand in Vienna to bring you the latest news from radiology’s premier pan-European conference. Stay tuned for our wrap-up newsletter next week, or follow along with our daily video reports on our LinkedIn and YouTube channels.

Microsoft Sunsets PowerScribe 360 Reporting Software

Posted on by Brian Casey

In a move sure to shake the fast-growing radiology reporting segment, Microsoft has begun notifying customers that it is retiring its PowerScribe 360 software and will end renewal and maintenance in August in favor of its newer cloud-based PowerScribe One reporting technology.

Microsoft began sending “end-of-life” letters to its customer base last week, confirming rumors circulating for months that it was backing away from PowerScribe 360. 

  • Microsoft is recommending that PowerScribe 360 customers transition to PowerScribe One, a newer cloud-based reporting solution available on a subscription basis rather than as an on-premises installation, as is the case with PowerScribe 360.

The company confirmed the news in an email to The Imaging Wire…

“Microsoft is retiring the on-premises product, PowerScribe 360, as part of a broader effort to ensure our customers continue to benefit from secure, future-ready solutions like PowerScribe One – which has cloud and AI capabilities at its core. This transition reflects our broader focus on providing solutions that empower healthcare organizations to meet the demands of modern care delivery securely and at scale. We are working closely with our customers to ensure a smooth transition.”

The news marks the end of the road for PowerScribe 360, which was originally developed by Nuance Communications and rose to become the dominant reporting solution for radiologists. 

  • Nuance launched PowerScribe 360 at RSNA 2010, and radiologists quickly adopted the technology, drawn to its improved speech recognition accuracy and structured reporting templates. Soon the company held 75% of the U.S. market for radiology reporting solutions.

Nuance introduced PowerScribe One in 2018 as the next generation of the software. Three years later Nuance was acquired by Microsoft and folded into Microsoft’s healthcare business. 

  • Microsoft’s strategy was to transition PowerScribe 360 users to PowerScribe One, which not only included newer tools but was also cloud-based with a regular subscription fee. This reportedly alienated many radiology customers who had already paid to have an on-premises reporting solution.  

Indeed, it only took a few years for rumors to begin circulating that Microsoft was looking to sunset PowerScribe 360 (despite many existing users), as evidenced by a recent Reddit thread on the topic. 

  • Last week’s EOL notifications inform customers that PowerScribe is being retired “as part of a broader effort to ensure our customers continue to benefit from secure, modern, and future ready solutions.” 

The letter goes on to state that PowerScribe users will need to convert to the latest version of PowerScribe One. This will require monthly payments even if they already “owned” PowerScribe 360.

  • What’s more, pricing agreements with Nuance or Microsoft will no longer be valid after the renewal date, and Microsoft will no longer provide support after the end-of-life date.

The news comes as radiology reporting is being transformed by new technology, particularly solutions driven by generative AI with large language models. 

  • Multiple startups are leveraging dissatisfaction with legacy solutions to offer reporting applications that promise more efficient workflow, and some offer better integration with image viewers and worklists to give radiologists a more unified reading experience. 

We’re also seeing a growing number of major PACS players announce new reporting solutions or outline future plans to add reporting capabilities, further complicating the market.

The Takeaway

The news that Microsoft is pulling the plug on PowerScribe 360 isn’t a surprise given the software’s age, persistent rumors of its demise, and Microsoft’s strategic focus on PowerScribe One. But it clears the field for what’s sure to be a scramble for the reporting application’s large market share.

When Radiologists Quit

Posted on by Brian Casey

The chance that a radiologist would quit their job for a new one doubled over a recent 10-year period. And a new JACR study identifies the exact point in terms of case workload when radiologists are most likely to leave.

The burnout epidemic among healthcare professionals has been closely tied to workload, which has been rising steadily due to growing patient volumes and ongoing staff shortages.

  • In radiology, the problem has been exacerbated as radiologists are reading more images (from more complex cases) while the number of new radiologists being trained in residency programs remains static.

In the new paper, researchers from the ACR’s Neiman HPI investigated changes in radiologist turnover from 2013 to 2022 and how they compared with workload as measured by work relative value units, the most standard measure of physician productivity. 

  • They analyzed data on services provided by 39.4k unique radiologists representing 280.7k radiologist-years over the study period, then correlated that with data on how often radiologists changed practices.

Researchers found…

  • The radiologist turnover rate increased 61% (from 5.3% to 8.5%).
  • Odds of radiologist turnover were nearly 2X in 2022 versus 2013 (OR = 1.96).
  • And were 6% higher for female radiologists and 12% higher for metropolitan versus nonmetropolitan radiologists.
  • While academic radiologists had 9% lower turnover odds than nonacademic imagers.

But what about the connection between workload and turnover? This is where the study gets interesting, as the researchers found a U-shaped relationship between the two.

At low wRVU levels, turnover tended to drop as workload went up, perhaps as radiologists found more job satisfaction (and maybe higher pay) with more work to do.

But this changed once wRVUs hit a threshold, and turnover began rising as well, apparently as radiologists found themselves overworked. This inflection point differed for different types of radiologists…

  • Occurring at 12.9k wRVUs for all radiologists.
  • But at 13.4k wRVUs for private-practice radiologists.
  • And only 8.8k wRVUs for academic radiologists.

The 34% lower wRVU threshold for academic radiologists could be because many have prioritized research and teaching, and see a growing clinical care workload as a distraction without commensurate compensation. 

The Takeaway

The new study offers a fascinating look at the forces driving when and why radiologists quit, and provides a new benchmark showing precisely where the breaking point is for most radiologists. Let’s hope this data is put to good use.  

Agentic AI for Radiology Follow-Up

Posted on by Brian Casey

Agentic AI has quickly become one of the hottest topics in radiology. But what is it really good for? Texas researchers offer one possible use case in a new study in NEJM Catalyst: scouring radiology reports to identify patients who require follow-up. 

Agentic AI is a new flavor of artificial intelligence that’s capable of working autonomously to complete tasks with minimal human supervision.

  • In healthcare, it’s being applied to a wide range of tasks, from improving health system operations to clinical and administrative jobs.

In the current study, researchers from Parkland Health in Dallas assigned agentic AI to one of the trickiest tasks in radiology: making sure patients with suspicious findings comply with recommendations for follow-up procedures.  

  • Previous studies have documented low rates of adherence to radiologist recommendations for follow-up imaging (possibly as low as 50%), creating the uncomfortable possibility of missed opportunities that could have major patient-care ramifications.

The dilemma can be compounded with the use of structured note templates in EHRs, as improper use or modification of these macros can lead to missed notifications. 

  • To address the problem, Parkland clinicians developed an AI agent based on a pretrained open-source large language model (Meta’s LLM Llama 3 70B) that reviews clinical impressions, extracts important details for follow-up, and integrates its findings into departmental workflow to enable patient outreach.

In tests on 10k radiologist notes, Parkland researchers found that their AI agent…

  • Had an overall detection rate of ~5.1%, slightly lower than other published studies (8% to 12%).
  • Had far higher sensitivity than Parkland’s previous macro-based follow-up notification system (99% vs. 16%), correctly flagging 6X more cases (513 vs. 83).
  • Achieved higher accuracy (99% vs. 58%), and 94% accuracy for characterizing follow-up timing, recommended procedure, and underlying abnormality. 

Considering Parkland’s annual volume of 500k imaging studies, the AI agent could identify 21.5k follow-up cases a year. 

  • Many of these could be serious issues, such as new cancer diagnoses or pathologies that require surgical intervention. 

The Takeaway

The new study shows that agentic AI isn’t some technogeek’s far-off dream – it’s a useful tool on the verge of real-world implementation, with the potential to improve patient care without overburdening radiology staff.

Mammo Screening Saves Lives – Even in Late-Stage Cancer

Posted on by Brian Casey

A new study confirms that not only does breast cancer screening save lives, but it also improves survival in women with late-stage disease. Researchers found that women with stage IV breast cancer had a survival rate over three times higher if their disease was detected with screening, thanks largely to its role in driving treatment.

The “Mammography Wars” over breast cancer screening’s effectiveness raged from the 1980s to the 2010s, but eventually were decided in mammography’s favor. 

  • Multiple research studies have demonstrated that the combination of early detection and more effective treatments improve breast cancer survival. The USPSTF’s 2023 shift back to recommending that screening start at 40 settled the issue. 

But pockets of anti-screening resistance remain, with screening skeptics publishing several studies since the USPSTF change questioning the value not only of mammography but also other cancer screening tests.

  • So it’s more important than ever to demonstrate cancer screening’s value.

The new study in the Journal of the National Cancer Institute does just that by analyzing screening’s impact on survival rates in women diagnosed with stage IV disease who had been invited to Denmark’s national breast screening program (not all women completed mammography despite getting invited).

  • In all, 32.8k women with breast cancer were included, of whom 8% presented with stage III or stage IV cancer. 

The researchers found that for women with stage IV breast cancer…

  • Five-year survival was over 2X higher for women with screen-detected cancer versus women who were never screened (75% vs. 32%).
  • Ten-year survival was over 3X higher (62% vs. 17%).
  • Women with later-stage disease detected by screening had survival rates over five years comparable to women with disease one stage lower who were never screened.
  • Survival rates were strongly influenced by treatment type, with surgical treatment showing the longest median survival versus non-surgical treatment and no treatment (6, 2, and 0.1 years, respectively).

The big difference in survival was driven by the fact that women with screen-detected cancers were far more likely to get surgical treatment, and to subsequently have better 10-year survival rates than those treated without surgery (60% vs. 8%).

The Takeaway

The new study once again proves the value of screening mammography, but it goes beyond just showing that screening causes a stage shift to earlier diagnosis. Even in women with late-stage disease, screening is driving more effective treatment that is proving invaluable in saving women’s lives.

Simpler Radiology Reports from LLMs

Posted on by Brian Casey

Can large language model AI algorithms write simpler radiology reports for patients than clinicians? A study published in European Radiology found that LLM-produced reports were more readable, but there are areas of concern that will require fine-tuning.

Patients are taking greater interest in managing their own healthcare, requesting direct access to medical information like images and reports.

  • That’s a good thing, but it creates challenges for healthcare professionals more used to communicating with other providers.

Taking the time to draft a report just for patients is a non-starter for many radiology professionals in a time of workforce shortages.

  • But this could be an excellent use case for AI, especially the LLMs that have sprung up over the past few years. 

So researchers from Germany tested three LLMs to draft patient-friendly versions of 60 radiology reports from X-ray, CT, MRI, and ultrasound modalities. 

  • The LLMs included the ubiquitous ChatGPT-4o, as well as two open-source LLMs (Llama-3-70B and Mixtral-8x22B) that had been deployed on-premises within their hospitals.

The authors wanted to know not only how well the LLMs performed in drafting patient reports, but also whether there were differences between the black-box ChatGPT 4o and the two open-source LLMs.

  • The LLMs were instructed to generate layperson summaries at the eighth-grade reading level, preserving key clinical information. 

In comparing original radiology reports to LLM-produced summaries, researchers found…

  • Original reports had much lower ease-of-reading scores on the Flesch readability scale (17 vs. 44-46).
  • Original reports were judged much less understandable on a five-point scale (1.5 vs. 4.1-4.4). 
  • The two open-source LLMs had higher rates of critical errors that could lead to patient harm (8.3%-10%), while ChatGPT 4o had no critical errors. 
  • Original reports had shorter total reading time versus LLM versions (15 vs. 64-73 seconds).
  • There was no difference in understandability based on modality.

The findings on critical errors are particularly concerning. 

  • Clinicians may see on-premises open-source LLMs as having patient privacy advantages over cloud-based ChatGPT 4o, but such models may require more clinical oversight to avoid patient harm. 

The Takeaway

The new study on LLM-generated patient radiology summaries is encouraging, pointing to a future in which a cumbersome task could be offloaded to generative AI algorithms. But much work remains to ensure patient safety and privacy before this can happen.

VC Funding Bounces Back in 2025

Posted on by Brian Casey

After a long slide, venture capital funding for medical imaging AI companies bounced back in 2025. That’s according to the latest report from market intelligence firm Signify Research. 

VC funding of AI startups has declined steadily since 2020, when cheap money fueled by low pandemic-era interest rates spurred a boom in both the total dollar value of investments as well as the number of funding rounds getting done.

  • Previous Signify reports documented the trend well, with the number of funding rounds peaking at nearly 80 in 2020 and total funding crossing the $1B mark in 2021. But by 2024, funding rounds had fallen by 64% and their dollar value by 70%.

But the numbers for 2025 show a turnaround starting, at least with respect to dollar value…

  • Total funding more than doubled compared to the year before ($709M vs. $336M).
  • While the number of funding rounds fell 17% (19 vs. 23).
  • But the size of the average funding round grew 112% ($39M vs. $19M).

In analyzing the numbers, Signify found that while funding momentum is coming back, investors are being more selective. 

  • Capital is concentrating in companies that have a clear enterprise fit, a strong integration pathway, and the ability to operate within platform and imaging IT ecosystems.

Funding rounds of note in 2025 included…

  • Aidoc’s haul of $150M.
  • An Ultromics funding that put the company in Signify’s coveted $100M club.
  • Cerebriu gaining over $10M in a Series A round.
  • a2z pulling in $4.5M in seed funding for its multi-triage platform. 

The report addresses turbulence in the AI platform sector, which saw significant disruption in 2025 after Bayer’s withdrawal from the market. 

  • Platform companies will need to move beyond AI orchestration and show they can actively improve radiology workflows and deliver better clinical decisions and measurable impact. 

The Takeaway

The 2025 bounceback in VC funding for AI firms is welcome news that the correction that followed the sugar high of 2020/2021 may have worked its way through the system. AI investments in 2026 are likely to be smarter and more focused, and in companies that have demonstrated their value in helping radiologists work more efficiently. 

CT Supports Better Stroke Care

Posted on by Brian Casey

When it comes to stroke, time is brain. And the faster stroke patients can be diagnosed, the sooner brain-saving treatment can start. Researchers in Germany found that sending stroke patients to hospitals equipped with CT scanners and telemedicine connections might be more effective than transferring them directly to specialized stroke centers.

CT is critical for assessing stroke patients and determining whether they should receive intravenous thrombolysis with clot-busting drugs or endovascular thrombectomy with catheter-guided devices.

  • It’s particularly important that patients be treated within the “golden hour” of stroke symptom onset, as every 10 minutes of delay results in eight weeks of healthy life lost.

Specialized stroke centers outfitted with dedicated equipment have sprung up to deliver better care, but they’re not that common and patient transfers can take extra time.

  • Far more common are hospitals with CT scanners, giving rise to the suggestion of a hub-and-spoke model in which patients are sent first to a hospital equipped with CT and telemedicine for diagnosis and initial thrombolysis (the spoke), and then on to a specialized center (the hub) if necessary.

This approach is tested in a new study in The Lancet Regional Health – Europe, in which German researchers performed a modeling study to see how hub-and-spoke stroke treatment compared to direct transfer to specialized stroke centers.

  • They developed a map of CT-equipped hospitals and dedicated stroke centers in Germany, and calculated minimum travel and time benefits in 10-minute thresholds.

The researchers found that of Germany’s population…

  • 76% were within 15 minutes of at least one hospital with on-site CT, and 99% were within 30 minutes.
  • 51% were within 15 minutes of a stroke-ready hospital (hospitals that treat a set number of stroke patients but aren’t yet certified), and 90% within 30 minutes.
  • Only 46% lived within 15 minutes of a stroke-certified hospital, a figure that grew to 85% within 30 minutes.
  • 36% would reach a CT-equipped hospital at least 10 minutes faster than a certified stroke unit.

Not surprisingly, there were geographic differences in accessibility, with urban areas having good access to specialized stroke centers but rural and underserved areas less so (90% vs. 55%).

  • So the hub-and-spoke model might be better suited for rural areas while the direct transfer approach would still work for urban zones. 

The Takeaway

While this study was conducted in Germany, its lessons could be applied to any country that has to juggle healthcare resources with clinical demands. The question is how much the findings might be impacted by new technologies like mobile stroke units and AI-based stroke assessment. 

Residency Push Skips Radiology

Posted on by Brian Casey

A federal push to alleviate the U.S. physician shortage by adding more resident training slots appears to have skipped radiology. Of the more than 400 residency programs awarded funding so far, only two diagnostic radiology programs got funds. 

The ongoing doctor shortage has become a major issue in U.S. healthcare, as physicians face rising patient volume from an aging population with a workforce that’s largely stagnant. 

  • Physicians are already experiencing high burnout rates, and the Association of American Medical Colleges predicts there will be a shortage of as many as 86k doctors by 2036.

Part of the problem is that physician training is tightly controlled in the U.S. Residency programs get most of their funding from Medicare, and there’s been a cap on the number of slots Medicare can fund since 1997.

  • So it takes an act of Congress – literally – to get more money to add residency slots.

That’s actually happened in recent years, with federal budget bills in 2021 and 2023 specifically allocating more money for Direct Graduate Medical Education to help train more residents through what’s commonly known as Section 126.

  • In all, the legislation is funding 1.2k new residency slots, with the positions released through five rounds of funding.

But the fourth round of new resident positions under Section 126, announced in December, skipped diagnostic radiology entirely. 

  • A list of the new positions by Becker’s Hospital Review found no diagnostic radiology slots added to U.S. resident training programs, while 20 interventional radiology positions were added. 

And over the course of the Section 126 program, only 0.5% of residency programs getting funding were diagnostic radiology.

It’s unclear how the omission occurred. Hospitals with resident training programs have to apply for the additional funding, and it’s possible that diagnostic radiology’s low (or nonexistent) numbers simply reflect fewer DR applications.

  • But it’s widely known that the federal government has prioritized training primary care physicians, as well as hospitals in rural areas. Indeed, being in a rural area or health professional shortage area are two of four ways for residency programs to qualify for Section 126 funding.

Legislation currently languishing in Congress – the Resident Physician Shortage Reduction Act of 2025 – would add 14k residency positions over the next seven years. 

  • But even such a large expansion in residency training won’t help medical imaging much if diagnostic radiology continues to get passed over when allocating new positions (the application period for the fifth and final round just opened). 

The Takeaway

The fact that diagnostic radiology is getting skipped over in Section 126 residency funding shows that there’s no cavalry coming over the hill to help the specialty deal with its workforce shortage. Help will have to come from somewhere else, be it AI, teleradiology, or some other kind of technology.

More Positive News on Mammo AI from MASAI

Posted on by Brian Casey

The latest results from the landmark MASAI study of AI for mammography screening show a favorable trend toward reducing the rate of interval cancers, or breast cancers that appear between screening rounds. The new findings – published Friday in The Lancet – also confirm mammography AI’s sharp workload reduction and trend toward higher sensitivity. 

MASAI is a large randomized controlled trial conducted in Sweden that examined the impact of ScreenPoint Medical’s Transpara AI algorithm on breast screening.

  • It’s an important issue, because mammography is one of the radiology segments where AI can provide the most help by reducing radiologist workload while improving cancer detection.

Previous MASAI studies demonstrated that AI can reduce radiologist workload by 44% and improve cancer detection rates by 28%.

  • The findings suggest that AI could eliminate the need for double-reading of most mammograms, a practice that’s common in European screening programs.

The new findings focus specifically on interval cancers, cancers that are missed in one screening round, only to be found later. 

  • Like other MASAI studies, the patient population consisted of 106k women screened with mammography and Transpara AI in Sweden’s national program in 2021 and 2022. 

Results indicated that AI-aided mammography…

  • Cut interval cancer rates by 12% per 1k women (1.55 vs. 1.76).
  • Reduced invasive interval cancers by 16% (75 vs. 89) with 27% fewer cancers of aggressive subtypes (43 vs. 59).
  • Detected 9% more cancers at screening (81% vs. 74%) with comparable specificity (99% for both) and recall rates (1.5% vs. 1.4%).

The researchers acknowledged that the study was not powered to show a statistically significant difference in the interval cancer rate. 

  • But its positive trend indicates that AI can be used to replace double-reading without negative consequences for patients – resulting in a sharp workload reduction for radiologists. 

The Takeaway

Results from the MASAI study on mammography AI just keep on getting better. Last week’s findings indicate that there’s really no reason for European breast screening programs to not dive in and replace their second readers with AI for the majority of exams.

Get every issue of The Imaging Wire, delivered right to your inbox.