Tag: fda

FDA Updates AI List with New Clearances

Posted on by Brian Casey

The FDA last week updated its list of cleared AI-enabled medical devices, with the new list showing AI marketing authorizations through the end of 2025. The updated list reveals that radiology is maintaining its lead as the medical specialty with the most clearances.

The FDA’s previous update featured data through the end of September 2025, and showed the number of AI-enabled medical devices for radiology crossed the 1k mark. The new numbers show continued momentum for medical imaging.

  • The agency’s data go all the way back to 1995 (the first cleared radiology device on the list was ImageChecker from R2 Technology/Hologic in 1998). 

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

  • Authorized 1,451 AI-enabled medical devices since it began keeping track in 1995.
  • Approved 1,104 radiology devices, or 76% of total AI-enabled medical authorizations.
  • In the fourth quarter of 2025, the FDA cleared 72 AI-enabled medical devices, of which 55 (76%) were radiology devices. 
  • For all of 2025, radiology secured 75% of authorizations, compared to 73% for all of 2024 and 80% for 2023. 
  • GE HealthCare retained the top spot as the company with the most radiology AI authorizations at 120 (including acquisitions Bay Labs, BK Medical, Caption Health, MIM Software, icometrix, and Spectronic Medical).
  • Next is Siemens Healthineers at 89 (including Varian), then Philips at 50 (including DiA Analysis and TomTec), Canon at 45 (including Vital Images and Olea), United Imaging at 38, Aidoc at 31, and DeepHealth at 28 (including Quibim and iCAD). 

As we’ve noted in the past, the FDA’s list includes not only standalone software applications, but also imaging hardware with embedded AI applications, such as a mobile X-ray system with AI algorithms for detecting emergent conditions. 

The Takeaway

The new FDA list shows radiology’s continued dominance when it comes to AI-enabled medical device technology. But an interesting subtext is the ongoing consolidation in the radiology AI space, which could mean that some firms may be climbing the list quickly.

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.

Radiology AI Approvals Near 1k in New FDA Update

Posted on by Brian Casey

The FDA last week released the long-awaited update to its list of AI-enabled medical devices that have received marketing authorization. The closely watched list shows the number of AI-enabled radiology authorizations approaching the 1k mark.

The FDA has been tracking authorizations of AI-enabled devices going back to 1995, and the list gives industry watchers a feel for not only how quickly the agency is churning out reviews but also which medical specialties are generating the most approvals.

  • But the last time the FDA released an updated list was August 2024, and recent turmoil at the agency had some observers wondering if it would continue the tradition – as well as whether it could stay on pace for new approvals.

Those fears should be assuaged with the new release. The numbers indicate that through May 2025 the FDA has…

  • Granted authorization to 1.2k AI-enabled medical devices since it started tracking.
  • Approved 956 AI-enabled radiology products, or 77% of total medical authorizations.
  • Radiology’s share of overall authorizations from January to May 2025 ticked up to 78% (115/148), compared to 73% in the 2024 update, and 80% in all of 2023.
  • GE HealthCare remains the company with the most radiology AI authorizations, at 96 (including recent acquisitions like Caption Health and MIM Software), with Siemens Healthineers in second place at 80 (including Varian). 
  • Other notable mentions include Philips (42 including DiA Analysis), Canon (35), United Imaging (32), and Aidoc (30). 

In a significant regulatory development, the FDA said it was developing a plan to identify and tag medical devices that use foundation models, including large language models and multimodal architecture. 

  • The agency said the program would help healthcare providers and patients know when LLM-based functionality was included in a medical device (the FDA has yet to approve a medical device with LLM technology). 

In another interesting change, the FDA dropped “machine learning” from the title of its list, apparently with the idea that “AI” was sufficient as an umbrella term. 

The Takeaway

The FDA’s release of its AI approval list is a welcome return to past practices that should reassure agency watchers that recent turmoil isn’t affecting its basic operations. The LLM guidance suggests the agency may be changing its approach to the technology in favor of disclosure and transparency instead of more stringent regulation that could delay some LLM solutions from reaching the market.

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.

How Should AI Be Monitored?

Posted on by Brian Casey

Once an AI algorithm has been approved and moves into clinical use, how should its performance be monitored? This question was top of mind at last week’s meeting of the FDA’s new Digital Health Advisory Committee.

AI has the potential to radically reshape healthcare and help clinicians manage more patients with fewer staff and other resources. 

  • But AI also represents a regulatory challenge because it’s constantly learning, such that after a few years an AI algorithm might be operating much differently from the version first approved by the FDA – especially with generative AI. 

This conundrum was a point of discussion at last week’s DHAC meeting, which was called specifically to focus on regulation of generative AI, and could result in new rules covering all AI algorithms. (An executive summary that outlines the FDA’s thinking is available for download.)

Radiology was well-represented at DHAC, understandable given it has the lion’s share of authorized algorithms (73% of 950 devices at last count). 

  • A half-dozen radiology AI experts gave presentations over two days, including Parminder Bhatia of GE HealthCare; Nina Kottler, MD, of Radiology Partners; Pranav Rajpurkar, PhD, of Harvard; and Keith Dreyer, DO, PhD, and Bernardo Bizzo, MD, PhD, both of Mass General Brigham and the ACR’s Data Science Institute.  

Dreyer and Bizzo directly addressed the question of post-market AI surveillance, discussing ongoing efforts to track AI performance, including … 

The Takeaway

Last week’s DHAC meeting offers a fascinating glimpse at the issues the FDA is wrestling with as it contemplates stronger regulation of generative AI. Fortunately, radiology has blazed a trail in setting up structures like ARCH-AI and Assess-AI to monitor AI performance, and the FDA is likely to follow the specialty’s lead as it develops a regulatory framework.

Why the FDA’s Density Rule Matters

Posted on by Brian Casey

The FDA’s new rules on reporting breast density to women getting mammograms went into effect on September 10. The implementation has been expected for some time, but this week’s rollout generated a wave of positive press coverage that highlights the importance both of breast density awareness and of breast screening.

The FDA in March 2023 said it would implement a national standard requiring providers to inform women of their breast density, which can obscure lesions on conventional X-ray mammography. 

  • Breast density is also a risk factor for cancer, and patient advocacy groups had been pressuring the FDA to set a standard to replace what has become a patchwork of state-by-state notification rules. 

The FDA’s rules have been incorporated into the Mammography Quality Standards Act, and require that … 

  • Mammography reports include a plain-language patient summary with “an overall assessment of breast density.” 
  • The summary must include specific language that defines breast density, explains its ramifications for detection and cancer risk, and suggests the need for additional imaging tests.

A novel aspect of the new rules is that they were mostly driven by patients – women like JoAnn Pushkin and the late Nancy Cappello who as patients discovered first-hand the shortcomings of X-ray-based mammography for women with dense breast tissue. 

What’s next? Density-awareness proponents are now turning their attention to reimbursement, which for supplemental imaging is inconsistent across the U.S.

  • A fix for the problem – the Find It Early Act – is working its way through Congress, and women’s health advocates lobbied on Capitol Hill this week to try to push the legislation through before the end of the current Congressional session. 

The new reporting landscape also creates opportunities for better software tools to detect and manage breast density and better predict risk in patients with dense breast tissue. 

  • Clinicians already realize that women with dense breasts not only need different screening modalities like MRI and ultrasound, but that they might also require more frequent screening due to their heightened cancer risk. 

The Takeaway

The FDA’s new breast density rules matter for a variety of reasons, from showing the power of patients to change their imaging experience to outlining a future in which risk plays a more prominent role in breast screening. While more work remains to be done, this is a good time to savor the triumph.

FDA Keeps Pace on AI Approvals

Posted on by Brian Casey

The FDA has updated its list of AI- and machine learning-enabled medical devices that have received regulatory authorization. The list is a closely watched barometer of the health of the AI sector, and the update shows the FDA is keeping a brisk pace of authorizations.

The FDA has maintained double-digit growth of AI authorizations for the last several years, a pace that reflects the growing number of submissions it’s getting from AI developers. 

  • Indeed, data compiled by regulatory expert Bradley Merrill Thompson show how the number of FDA authorizations has been growing rapidly since the dawn of the medical AI era in around 2016 (see also our article on AI safety below). 

The new FDA numbers show that …

  • The FDA has now authorized 950 AI/ML-enabled devices since it began keeping track
  • Device authorizations are up 15% for the first half of 2024 compared to the same period the year before (107 vs. 93)
  • The pace could grow even faster in late 2024 – in 2023, FDA in the second half authorized 126 devices, up 35% over the first half
  • At that pace, the FDA should hit just over 250 total authorizations in 2024 
  • This would represent 14% growth over 220 authorizations in 2023, and compares to growth of 14% in 2022 and 15% in 2021
  • As with past updates, radiology makes up the lion’s share of AI/ML authorizations, but had a 73% share in the first half, down from 80% for all of 2023
  • Siemens Healthineers led in all H1 2024 clearances with 11, bringing its total to 70 (66 for Siemens and four for Varian). GE HealthCare remains the leader with 80 total clearances after adding three in H1 2024 (GE’s total includes companies it has acquired, like Caption Health and MIM Software). There’s a big drop off after GE and Siemens, including Canon Medical (30), Aidoc (24), and Philips (24).

The FDA’s list includes both software-only algorithms as well as hardware devices like scanners that have built-in AI capabilities, such as a mobile X-ray unit that can alert users to emergent conditions. 

  • Indeed, many of the authorizations on the FDA’s list are for updated versions of already-cleared products rather than brand-new solutions – a trend that tends to inflate radiology’s share of approvals.

The Takeaway

The new FDA numbers on AI/ML regulatory authorizations are significant not only for revealing the growth in approvals, but also because the agency appears to be releasing the updates more frequently – perhaps a sign it is practicing what it preaches when it comes to AI openness and transparency. 

Is Radiology’s AI Edge Fading?

Posted on by Brian Casey

Is radiology’s AI edge fading, at least when it comes to its share of AI-enabled medical devices being granted regulatory authorization by the FDA? The latest year-to-date figures from the agency suggest that radiology’s AI dominance could be declining. 

Radiology was one of the first medical specialties to go digital, and software developers have targeted the field for AI applications like image analysis and data reconstruction.

  • Indeed, FDA data from recent years shows that radiology makes up the vast majority of agency authorizations for AI- and machine learning-enabled medical devices, ranging from 86% in 2020 and 2022 to 79% in 2023. 

But in the new data, radiology devices made up only 73% of authorizations from January-March 2024. Other data points indicate that the FDA …

  • Authorized 151 new devices since August 2023
  • Reclassified as AI/ML-enabled 40 devices that were previously authorized 
  • Authorized a total of 882 devices since it began tracking the field 

      In an interesting wrinkle, many of the devices on the updated list are big-iron scanners that the FDA has decided to classify as AI/ML-enabled devices. 

      • These include CT and MRI scanners from Siemens Healthineers, ultrasound scanners from Philips and Canon Medical Systems, an MRI scanner from United Imaging, and the recently launched Butterfly iQ3 POCUS scanner. 

      The additions could be a sign that imaging OEMs increasingly are baking AI functionality into their products at a basic level, blurring the line between hardware and software. 

      The Takeaway

      It should be no cause for panic that radiology’s share of AI/ML authorizations is declining as other medical specialties catch up to the discipline’s head start. The good news is that the FDA’s latest figures show how AI is becoming an integral part of medicine, in ways that clinicians may not even notice.

      Unpacking the Biden Administration’s New AI Order

      Posted on by Brian Casey

      It seems like watershed moments in AI are happening on a weekly basis now. This time, the big news is the Biden Administration’s sweeping executive order that directs federal regulation of AI across multiple industries – including healthcare. 

      The order comes as AI is becoming a clinical reality for many applications. 

      • The number of AI algorithms cleared by the FDA has been surging, and clinicians – particularly radiologists – are getting access to new tools on an almost daily basis.

      But AI’s rapid growth – and in particular the rise of generative AI technologies like ChatGPT – have raised questions about its future impact on patient care and whether the FDA’s existing regulatory structure is suitable for such a new technology. 

      The executive order appears to be an effort to get ahead of these trends. When it comes to healthcare, its major elements are summarized in a succinct analysis of the plan by Health Law Advisor. In short, the order: 

      • Calls on HHS to work with the VA and Department of Defense to create an HHS task force on AI within 90 days
      • Requires the task force to develop a strategic plan within a year that could include regulatory action regarding the deployment and use of AI for applications such as healthcare delivery, research, and drug and device safety
      • Orders HHS to develop a strategy within 180 days to determine if AI-enabled technologies in healthcare “maintain appropriate levels of quality” – basically, a review of the FDA’s authorization process
      • Requires HHS to set up an AI safety program within a year, in conjunction with patient safety organizations
      • Tells HHS to develop a strategy for regulating AI in drug development

      Most analysts are viewing the executive order as the Biden Administration’s attempt to manage both risk and opportunity. 

      • The risk is that AI developers lose control of the technology, with consequences such as patients potentially harmed by inaccurate AI. The opportunity is for the US to become a leader in AI development by developing a long-term AI strategy. 

      The Takeaway

      The question is whether an industry that’s as fast-moving as AI – with headlines changing by the week – will lend itself to the sort of centralized long-term planning envisioned in the Biden Administration’s executive order. Time will tell.

      FDA Data Show AI Approval Boom

      Posted on by Brian Casey

      In the previous issue of The Imaging Wire, we discovered how venture capital investment in AI developers is fueling rapid growth in new AI applications for radiologists (despite a slowdown this year). 

      This trend was underscored late last week with new data from the FDA showing strong growth in the number of regulatory authorizations of AI and machine learning-enabled devices in calendar 2023 compared to the year before. The findings show:

      • A resurgence of AI/ML authorizations this year, with over 30% growth compared to 14% in 2022 and 15% in 2021 – The last time authorizations grew this fast was in 2020 (+39%)
      • The FDA authorized 171 AI/ML-enabled devices in the past year. Of the total, 155 had final decision dates between August 1, 2022 to July 30, 2023, while 16 were reclassifications from prior periods 
      • Devices intended for radiology made up 79% of the total (122/155), an impressive number but down slightly compared to 87% in 2022 
      • Other medical specialities include cardiology (9%), neurology (5%), and gastroenterology/urology (4%)

      One interesting wrinkle in the report was the fact that despite all the buzz around large language models for generative AI, the FDA has yet to authorize a device that uses generative AI or that is powered by LLMs. 

      The Takeaway

      The FDA’s new report confirms that radiology AI shows no sign of slowing down, despite a drop in AI investment this year. 

      The data also offer perspective on a JACR report last week predicting that by 2035 radiology could be seeing 350 new AI/ML product approvals for the year. Product approvals would only have to grow at about a 10% annual rate to hit that number – a figure that seems perfectly achievable given the new FDA report.

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