Tag: radiology AI

FDA AI Approvals Surge Past 1k for Radiology

Posted on by Brian Casey

The number of AI-enabled medical devices granted FDA marketing authorization for radiology surged past the 1k mark in the latest update from the agency. The numbers show that radiology’s share of authorizations remains stable at just over three-quarters of total approvals.

The FDA regularly releases the list in what’s become a closely watched barometer of both total approvals as well as which medical specialties are most active in AI.

  • Radiology has historically garnered the lion’s share of approvals – perhaps no surprise given the discipline’s early adoption of both digital image management and AI – with the first authorization granted in 1998 (for ImageChecker mammography CAD from R2 Technology/Hologic). 

The new list tracks authorizations through the end of September 2025, and indicates the agency has…

  • Authorized 1,356 AI-enabled devices since it started tracking, up 8.5% since its last report.
  • Approved 1,039 AI-enabled radiology devices, with imaging accounting for 77% of total medical authorizations since 1998.
  • Radiology secured 75% of total authorizations from June to September (83/110), compared to 78% from January to May 2025, 73% for all of 2024, and 80% for 2023. 
  • GE HealthCare retains the top spot as the company with the most radiology AI authorizations, at 115 (including recent acquisitions Bay Labs, BK Medical, Caption Health, MIM Software, icometrix, and Spectronic Medical).
  • Next is Siemens Healthineers at 86 (including Varian), then Philips at 48 (including DiA Analysis and TomTec), Canon at 41 (including Vital Images and Olea), United Imaging at 38, and Aidoc at 30. 

As always, it’s worth noting that the FDA’s list includes not only standalone software applications, but also imaging equipment that might have AI applications embedded into it, such as a mobile X-ray system with AI algorithms for detecting emergent conditions. 

  • Also, the agency noted that it is exploring ways to identify and tag AI-based devices that use foundation models and large language models. The FDA has yet to approve an LLM-based medical device.

The Takeaway

The new numbers indicate that radiology’s dominance of medical AI continues. But they also show that the FDA has returned to a regular twice-yearly cadence of updating its list of AI-enabled medical devices after a break of nearly a year – news that’s welcome to AI developers.

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. 

Could States Take Over AI Regulation from the FDA?

Posted on by Brian Casey

Could states take over AI regulation from the FDA as a possible solution to the growing workforce shortage in radiology? It may seem like a wild idea at first, but it’s a question proposed in a special edition of Academic Radiology focusing on radiology and the law. 

Healthcare’s workforce shortage is no secret, and in radiology it’s manifested itself with tight supplies of both radiologists and radiologic technologists. 

  • AI has been touted as a potential solution to lighten the workload, such as by triaging images mostly likely to be normal from requiring immediate radiologist review. 

And autonomous AI – algorithms that operate without human oversight – are already nibbling at radiology’s fringes, with at least one company claiming its solution can produce full radiology reports without human intervention.

  • But the FDA is notoriously conservative when it comes to authorizing new technologies, and AI is no exception. So what’s to stop a state facing a severe radiologist shortage from adopting autonomous AI on its own to help out? 

The new article reviews the legal landscape behind both constitutional and state law, finding examples in which some states have successfully defied federal regulation – such as by legalizing marijuana use – if the issue has broad public support. 

But the authors eventually answer their own question in the negative, stating that it’s not likely states will usurp the FDA’s role regulating AI because…

  • The U.S. Constitution’s Supremacy and Commerce clauses ensure federal law will always supersede state law.
  • If AI made an error, malpractice regulation would be murky given a lack of legal precedent at the state level. 
  • Teleradiologists could opt out of providing care to a state if AI regulations were too burdensome – which could exacerbate the workforce crisis. 

The Takeaway

Ultimately, it’s not likely states will take over AI regulation from the FDA, even if the healthcare workforce shortage worsens significantly. But the Academic Radiology article is an interesting thought experiment that – in an environment in which U.S. healthcare policies have already been turned upside down – may not be so unthinkable after all. 

AI Predicts Radiology Workload

Posted on by Brian Casey

AI is touted as a tool that can help radiologists lighten their workload. But what if you could use AI to predict when you’ll need help the most? Researchers in Academic Radiology tried that with an AI algorithm that predicted radiology workload based on three key factors. 

Imaging practices are facing pressure from a variety of forces that include rising imaging volume and workforce shortages, with one recent study documenting a sharp workload increase over the past 10 years.

  • Many industry observers believe AI can assist radiologists in reaching faster diagnoses, or by removing studies most likely to be normal from the worklist based on AI analysis. 

But researchers and vendors are also developing AI algorithms for operational use – arguably where radiology practices need the most help.

  • AI can predict equipment utilization, or even create a virtual twin of a radiology facility where administrators can adjust various factors like staffing to visualize their impact on operations.

In the new study, researchers from Mass General Brigham Hospital developed six machine learning algorithms based on a year of imaging exam volumes from two academic medical centers.

The group entered 707 features into the models, but ultimately settled on three main operational factors that best predicted the next weekday’s imaging workload, in particular for outpatient exams…

  • The current number of unread exams.
  • The number of exams scheduled to be performed after 5 p.m.
  • The number of exams scheduled to be performed the next day.

The algorithm’s predictions were put into clinical use with a Tableau dashboard that pulled data from 5 p.m. to 7 a.m. the following day, computed workload predictions, and output its forecast in an online interface they called “BusyBot.”

  • But if you’re only analyzing three factors, do you really need AI to predict the next day’s workload? 

The authors answered this question by comparing the best-performing AI model to estimates made by radiologists from just looking at EHR data. 

  • Humans either underestimated or overestimated the next day’s volume compared to actual numbers, leading the authors to conclude that AI did a better job of calculating dynamics and weighting variables to produce accurate estimates.

The Takeaway

Using AI to predict the next day’s radiology workload is an intriguing twist on the argument that AI can help make radiologists more efficient. Better yet, this use case helps imagers without requiring them to change the way they work. What’s not to like?

AI and Legal Liability in Radiology

Posted on by Brian Casey

What impact will artificial intelligence have on the legal liability of the radiologists who use it? A new study in NEJM AI suggests that medical malpractice juries may pass harsher judgment on radiologists when they make mistakes that disagree with AI findings.

AI is viewed as a technology that can save radiologists time while also helping them make more accurate diagnoses.

  • But there’s a dark side to AI as well – what happens when AI findings aren’t correct, or when radiologists disagree with AI only to discover it was right all along?

In the new study, a research team led by Michael Bernstein, PhD, of Brown University queried 1.3k U.S. adults on their attitudes toward radiologists’ legal liability in two clinical use cases for AI – identifying brain bleeds and detecting lung cancers.

  • Participants were asked if they felt radiologists met their duty of care to patients across different scenarios, such as whether the AI and the radiologist agreed or disagreed on the original diagnosis. 

Responses were compared to a “no AI” control scenario in which respondents assessed legal liability if radiologists hadn’t used AI at all, with researchers finding …

  • If radiologists disagreed with AI, more respondents found radiologists liable …
    • Brain bleeds: 73% found radiologist liable (vs. 50% with no AI)
    • Lung cancer: 79% found radiologist liable (vs. 64% with no AI)
  • If both radiologists and AI missed the diagnosis, there was no statistically significant difference …
    • Brain bleeds: (50% vs. 56% with no AI, p=0.33)
    • Lung cancer: (64% vs. 65% with no AI, p=0.77)
  • Respondents were less likely to side with plaintiffs when given information about standard AI error rates …
    • When AI agreed with the radiologist diagnosis:
      • Brain bleeds: (73% plaintiff agreement fell to 49%)
      • Lung cancer: (79% fell to 73%)
    • When AI disagreed with the radiologist diagnosis:
      • Brain bleeds: (50% plaintiff agreement fell to 34%)
      • Lung cancer: (64% fell to 56%)

The Takeaway

The new study offers a fascinating look at AI’s future in radiology from a medico-legal perspective. But there’s one question the researchers didn’t address: If AI-supported image interpretation eventually becomes the standard of care, will radiologists be found liable for not using it at all? Stay tuned. 

Reporting Rules at SIIM 2025

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine offered a great opportunity to take stock of the imaging IT segment. At SIIM 2025, radiology reporting solutions – many powered by AI – were among the most exciting technologies under discussion at Portland’s Oregon Convention Center. 

As we mentioned in our video highlights roundup, attendance seemed a bit lighter at SIIM 2025, perhaps due to the Portland location and timing before a holiday weekend. 

  • But the number of vendors exhibiting at SIIM 2025 cracked 100 for the first time in years, underscoring the meeting’s importance as well as the overall growth of the imaging IT segment as the rise of AI spurs startup creation.

Every SIIM conference provides a fascinating early look at the trends and technologies that will shape radiology’s future, and this year’s meeting was no exception … 

  • Radiology Reporting Rules. The report is the radiologist’s final product, and SIIM 2025 presentations highlighted how important it is to improve this process, especially with AI. An entire track on May 21 was devoted to AI-enhanced reporting solutions, and on the exhibit floor companies showed AI-enhanced solutions that interpret radiologist findings and create structured reports from them. 
  • Questions about AI Adoption. As with past SIIM conferences, questions persist about the pace of AI adoption as well as the FDA’s regulatory direction since the Trump Administration took over. In SIIM 2025’s keynote address, health policy expert Rohini Kosoglu urged SIIM and the radiology community to take a more active role in self-regulation of AI in the absence of stronger direction from the federal government. 
  • Cloud Adoption Gains Steam. There are no such doubts about cloud-based image management, as providers are getting over past concerns about the technology. One enterprise image management vendor told The Imaging Wire that 100% of their new system orders included some form of cloud component. On the other hand, imaging IT expert Herman Oosterwijk sees some imaging sites having “second thoughts” about cloud hosting. 

The Takeaway

The growing prominence of radiology reporting software at SIIM 2025 illustrates the heightened interest in imaging IT solutions that enhance radiologist productivity rather than assist them with interpreting images – a job many feel they can do well enough on their own. 

SIIM 2025 Video Highlights

Posted on by Brian Casey

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

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

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

The Takeaway 

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

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

VC AI Funding Plummets

Posted on by Brian Casey

If you thought venture capital funding of AI firms was lower last year, you weren’t wrong. A new report from market analysis firm Signify Research found that VC funding of radiology AI firms dropped by nearly half in 2024 compared to the year before. 

VC funding has become a closely watched barometer of the radiology AI segment’s overall health. 

  • As most AI developers haven’t generated significant cash flows from product revenues yet, VC money is the oxygen that keeps AI firms breathing. 

And there are signs that after peaking in 2021, that oxygen is coming into short supply. 

  • Signify’s report last year documented a 19% drop in VC AI funding in 2023, a development attributed to tighter access to capital due to high interest rates. 

Those trends continued into 2024, with the new Signify report finding …

  • Total VC funding was $335.5M, down 48% compared to $645.6M in 2023.
  • The number of funding rounds fell 35% (20 vs. 31), to the lowest level since 2015.
  • Average deal size fell 16% ($16.8M vs. $20.1M).
  • Cleerly raised the most in 2024 with $106M in funding, followed by Qure.ai with $65M (putting Qure into Signify’s elite “$100M Club”). 
  • Funding declines were even worse in the Asia-Pacific (-84%) and the Europe, Middle East, and Africa regions (-76%) compared to the peak in 2021. 

Signify analyst Umar Ahmed noted that 2025 got off to a strong start, with $100M in funding rounds announced in January.

  • This could either represent a rebound in investor confidence, or indicate that the AI funding cycle is getting longer as VC firms put developers under the microscope and demand better ROI for their investments. 

The Takeaway

So it’s agreed that 2024 was a wash for VC radiology AI funding – what about 2025? The year’s strong start appears to have petered out as we approach the spring quarter, and ongoing regulatory turbulence and economic uncertainty in the U.S. isn’t likely to help. Stay tuned. 

Will FDA Staff Cuts Slow AI Adoption?

Posted on by Brian Casey

The Trump Administration’s campaign to cut the federal workforce arrived at the FDA last weekend – in particular its division regulating AI in healthcare. Multiple staff cuts were reported at the Center for Devices and Radiological Health, which had been in the midst of a major overhaul of AI regulation. 

A February 15 article in STAT News first reported the layoffs, which as with other recent staff reductions concentrated on FDA employees with probationary status and was part of a larger initiative that has also affected the CDC and NIH. 

The rapid growth of medical AI has had a major impact on the center, which as of its last report had given regulatory authorization to over 1k AI-enabled devices (76% of which are for radiology). 

  • To deal with the deluge, CDRH reportedly had been hiring many new staffers who were still on probationary status, making them targets for layoffs (permanent federal employees have civil service protections that make them harder to fire). 

FDA also has been retooling its regulatory approach to AI with new initiatives that reflect the fact that AI products continue learning (and changing) after they’ve been approved, and thus require more aggressive post-market surveillance than other medical devices…

So what impact – if any – will the layoffs have on the rapidly growing medical AI segment? 

  • The FDA may simply scale back its new AI initiatives and regulate the field under more traditional avenues that have served the medical device industry well for decades.

In another scenario, the FDA’s frenzied pace of AI approvals and initiatives could slow as the agency struggles to handle a growing number of product submissions with less staff. 

The Takeaway

The FDA layoffs couldn’t have come at a worse time for medical AI, which is on the cusp of wider clinical acceptance but still suffers from shaky confidence and poor understanding on the part of both providers and patients (see story below). The question is whether providers, organized radiology, or developers themselves will be able to step into the gap being left.

AI Guides Lung Ultrasound

Posted on by Brian Casey

Healthcare professionals with no experience in lung ultrasound were able to acquire diagnostic-quality scans comparable to those of experts thanks to AI guidance in a new paper in JAMA Cardiology. 

Ultrasound is one of the most versatile and cost-effective imaging modalities, but it is operator-dependent and many of its more challenging clinical applications require highly trained personnel. 

  • Echocardiography AI has already been shown to help novice healthcare personnel improve their skill to that of expert users – could AI also have applications in other areas, like lung ultrasound? 

To find out, researchers used Caption Health’s AI technology to guide lung ultrasound scans in 176 patients with clinical concerns for pulmonary edema from July to December 2023. 

  • Patients were scanned twice, once by an expert in lung ultrasound without AI guidance and once by a healthcare professional (registered nurses or medical assistants without formal ultrasound training) who received a short training session with lung guidance AI software. 

In analyzing the results, the researchers found …

  • Nearly all the scans acquired by healthcare professionals with AI assistance were of diagnostic quality. 
  • There was no statistically significant difference in quality between scans acquired by healthcare personnel and those of experts (98% vs. 97%, p=0.31).
  • AI-aided personnel actually performed better than experts in the lung area around the heart (91% vs. 77%), perhaps due to AI guidance. 
  • At 15 minutes, median scan acquisition times were longer than those reported in the literature (six and eight minutes). 

The findings could have major implications around access-to-care issues, with handheld ultrasound scanners distributed to low-resource areas where AI-guided healthcare professionals could perform scans sent to tertiary care centers for interpretation. 

The Takeaway

The new study demonstrates an exciting use case for AI in ultrasound that builds on previous research in echo AI. By giving more healthcare professionals access to the power of ultrasound, it promises to democratize access to care in many resource-challenged areas.  

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