radiology AI Archives - The Imaging Wire

Blackford Finds Buyer with Sopris Acquisition

Posted on October 11, 2026October 16, 2026 by Brian Casey

One of radiology’s longest-running mysteries was resolved late last week when a U.S. private equity firm acquired AI platform company Blackford Analysis from Bayer. Sopris Capital said it would fold Blackford into another radiology AI property it owns, Azra AI, as it builds its healthcare technology portfolio.

The news comes just over a year after Bayer made the stunning announcement that it was backing away from the AI platform business and would “discontinue offerings and services” in the sector, including Blackford.

The move reversed several years of Bayer’s investment in digital platforms, including the company’s own Calantic Digital Solutions division, which had been formed in 2022.

The announcement left Blackford twisting in the wind. Some industry experts questioned the long-term viability of the AI platform model, in which platform companies act as middlemen between algorithm developers and end-users, adding a markup that made AI deployment more expensive.

But others still see AI platforms and algorithm orchestration as a necessary foundation for successful AI deployment, especially given the explosion of clinical algorithms and the complexity of IT integration.

For its part, early reports following last year’s Bayer announcement suggested that Blackford’s business model was sound, with the company reporting a strong pipeline of prospective customers.

And despite the departure of key executives like founder Ben Panter, Blackford continued operations under new managing director James Holroyd. The company even exhibited at SIIM 2026 – not exactly a sign of a firm about to go under. And not long after SIIM, Blackford announced a new partnership with Azra AI – perhaps a portent of last week’s announcement.

So what do we know about Sopris? With its main offices in Colorado and Florida, Sopris first invested in Azra AI in 2022 to help the company acquire Digital Reasoning and expand into oncology.

Also in 2022, Sopris helped enterprise image management company PaxeraHealth close a Series A funding round to ramp up its activities in AI.

Sopris sees the Blackford acquisition as adding an AI orchestration capability to its growing imaging IT portfolio, combining Blackford with Azra AI.

Blackford will remain a separate legal entity with operations in Scotland and the U.S., and will continue serving its existing OEM partners and health system customers “with no disruption to current deployments or operations.”

The Takeaway

The Sopris acquisition of Blackford means that one of the radiology AI sector’s most established companies will continue operations for the foreseeable future. It also raises the intriguing prospect that a new player is entering the game to drive AI consolidation.

Measuring AI’s Impact in Real-World Radiology

Posted on September 30, 2026September 30, 2026 by Brian Casey

One of the criticisms of AI in radiology is that there isn’t much data on its effectiveness in real-world environments. Well, a new study in JACR aims to fill that gap by detailing how AI adoption at a large imaging network in Europe affected key radiology performance metrics.

Plenty of studies have been published on AI’s positive contribution to radiologists’ diagnostic performance.

But in most cases this research was performed under controlled conditions, or involved a single AI algorithm working on a particular task. That’s left many wondering how well AI would perform under actual conditions.

In the new study, researchers tracked the performance of 10 AI algorithms from seven vendors that were implemented over the Incepto Medical AI orchestration platform at 3R Swiss Imaging Network, a 20-center outpatient radiology network in Switzerland.

Over 4.5 years, researchers tracked the network’s performance before and after AI implementation for the major radiology key performance indicator, report turnaround time (TAT). They also tracked AI adoption rates and infrastructure latency, or how delays in data transfer between PACS networks affected AI usage.

In all, 389k AI-assisted imaging exams were processed over the implementation period from 2021 to 2025, with researchers finding…

AI’s biggest impact was in high-volume modalities, reducing TAT for trauma radiography (-26%) and knee MRI (-18%).
AI improved the network’s diagnostic capacity, performing the work of almost half a full-time-equivalent radiologist for trauma radiography alone (0.46 FTE).
AI usage rates were high, with 91% reporting active AI adoption and 66% reporting regular use.

On the downside, latency in data transfer between PACS networks slowed down AI performance, and in some cases AI results arrived after reports were finalized – too late to make a clinical impact…

Median total latency was 2.06 minutes per exam, with 72% attributable to data routing and tasks like fetching studies from PACS.
AI’s “too late rate” was 7.2% overall, ranging from 13% for chest CT to 3% for knee MRI, with 6.8% for trauma X-ray.

How did the network’s radiologists feel about AI? Researchers surveyed over 50 radiologists working for the network, finding that most of the differences in before-and-after opinions weren’t statistically significant.

The only difference was a modest gain in the perception that AI made radiologists more productive (from 2.57 to 2.94 on a five-point Likert scale).

The Takeaway

The new study fills in a major gap in real-world experience regarding radiology AI implementation. It also reveals areas for improvement, as slow data transfer rates between networks can prevent AI results from reaching radiologists in time.

Top 10 AI Vendors by FDA Approvals

Posted on September 13, 2026September 13, 2026 by Brian Casey

The FDA updated its list of regulatory authorizations for AI-enabled medical devices, covering approvals granted through the end of June 2026. The new numbers show radiology maintaining its strong lead over other medical specialties.

The FDA issues quarterly updates on marketing authorizations for AI-enabled products, a category that includes dedicated software applications as well as AI-enhanced hardware, like scanners with onboard AI functionality.

The list is closely watched by healthcare industry observers, and radiology has typically secured more than three-quarters of the authorizations – not surprising given the specialty’s image-intensive nature.

The most recent list tracks FDA actions through the end of June 2026, indicating that the FDA has…

Authorized 1,614 AI-enabled medical devices since it started keeping track, up 5.9% from Q1 2026.
Authorized 1,230 radiology devices, or 76% of all AI-enabled medical authorizations.
In the second quarter of 2026, the FDA authorized 89 AI-enabled medical devices, slightly fewer than the 92 devices it authorized in the first quarter of 2026.
For the quarter, 66 authorizations (74%) were for radiology devices, similar to the Q1 ratio (75%).
GE HealthCare maintained its AI authorizations lead with 134, thanks in part to approvals secured by companies it has acquired over the years.
Next are Siemens Healthineers at 101, then Philips at 62, Canon at 51, United Imaging at 45, Aidoc at 34, and DeepHealth at 32, with all numbers including acquisitions.
Rounding out the top 10 are Samsung (21), RapidAI (20), and Hyperfine (13).

The Takeaway

The new numbers track pretty closely with data on AI-enabled regulatory authorizations the FDA released for the first quarter. It will be interesting to see whether the agency’s pace changes as it comes to a final decision on regulating generative AI algorithms used for direct clinical care – a task for which it is currently soliciting feedback.

Harrison’s Teleradiology Play

Posted on September 9, 2026September 10, 2026 by Brian Casey

AI developer Harrison.ai is expanding its business model by supporting a teleradiology venture called Frontier Radiology that’s scaling up and hiring radiologists. But the move drew unwanted attention from a critical article in the Australian news media this week.

Harrison is best known for its suite of AI algorithms for radiology applications, as well as its Harrison.Rad 1.5 foundation model that can draft reports for radiologist review.

Harrison is based in Australia, but has recently made the U.S. market a major focus, with co-founder and managing director Dimitry Tran relocating to California to spearhead the initiative.

But it was Harrison’s newfound U.S. focus that apparently drew the attention of ABC News, an Australian news outlet that on September 7 published a critical article that questioned the company’s moves, in particular its involvement with Frontier Radiology.

One bone of contention in the story was whether Harrison’s U.S. emphasis was appropriate given the Australian government’s 2025 investment of US$23M in the company. The story also claimed Harrison was planning layoffs of Australian staff and that its integration of AI with Frontier’s clinical services could be a conflict of interest.

But in an interview with The Imaging Wire, Dimitry Tran clarified Harrison’s strategic direction and its involvement with Frontier Radiology.

Tran noted that Frontier is owned and operated by a radiologist – Joshua Ewell, DO – in accordance with U.S. rules requiring physician ownership of entities providing clinical services.

Ewell’s LinkedIn profile says Frontier will be an “AI-native radiology group” built entirely around AI foundation models.

Harrison is providing Frontier with non-clinical services, including its suite of AI algorithms, which are already helping Frontier teleradiologists achieve industry-leading report turnaround times. But Frontier radiologists are free to use any AI solutions they wish – including those of Harrison’s competitors.

Tran noted that close cooperation between AI developers and imaging services providers is hardly unusual anymore in radiology.

Indeed, two of the specialty’s largest U.S. providers – Radiology Partners and RadNet – have formed their own AI divisions to provide algorithms to both their own radiologists and outside customers.

The ABC News article conflated a number of recent developments into a narrative that doesn’t reflect reality, Tran believes.

For example, the layoffs that occurred earlier this year at Harrison were connected to the company’s transition from an aggressive R&D phase into a commercialization push and weren’t related to Harrison’s U.S. entry.

And the Australian government’s funding was part of an investment that gave it a single-digit equity stake in the company – a stake it retains to this day and that will prove increasingly profitable with Harrison’s growing success.

Even as it supports Frontier, Harrison plans to continue its focus on AI algorithm development and commercialization, especially of the Harrison.Rad foundation model – while keeping the “vast majority” of its employees in Australia.

The Takeaway

Putting aside the ABC article’s negative spin, Harrison’s move into teleradiology offers an intriguing twist on the growing integration between AI and imaging services providers. Given ongoing workforce shortages and rising imaging volume, it’s perhaps the best way to move the chains toward finding relief for beleaguered radiologists.

AI Reduces Mammography Workload

Posted on May 31, 2026May 31, 2026 by Brian Casey

Using AI to triage low-risk breast screening exams that don’t need extra review could remove more than three-quarters of mammography cases from radiologists’ workload and allow them to spend more time on high-risk cases. That’s according to a new study in Radiology: Artificial Intelligence that confirms other recent studies.

Much of recent mammography AI research has focused on its ability to triage low-risk cases to avoid additional radiologist review – saving precious personnel resources.

This is particularly valuable in Europe, which uses a double-reading paradigm in which two radiologists review all mammography cases (the U.S. employs single readers but tends to screen women annually rather than every two years).

The new study comes from France, which employs a slightly different paradigm from the rest of Europe. Double reading is conducted only for lower-risk BI-RADS 1 and 2 cases, while BI-RADS 3-5 go directly to diagnostic workup.

As such, double reading occurs with cases that have low cancer prevalence, which can make it more difficult for radiologists to detect cancers that don’t occur very often.

But what if you offloaded low-risk double reading to AI?

In the new paper, researchers tried that with Therapixel’s MammoScreen AI algorithm, which was employed retrospectively to analyze mammograms from 42.4k women acquired from 2015 to 2019.

AI results were compared to standard radiologist double reading, with the following findings…

AI classified 77% of cases as low-risk, meaning these could be safely triaged from the double-reading paradigm.
AI missed only one cancer in the low-risk group, a rate the researchers characterized as “small but measurable.”
Eleven cancers were found in the group AI classified as non-low-risk, which would have undergone double reading anyway in the AI triage paradigm.
Rates of interval cancer (cancer that occurs between screening rounds) were 5X higher in the cases AI classified as non-low-risk compared to low-risk (2.16 vs. 0.47 cancers per 1k exams).

Using AI to classify and remove low-risk cases from double reading could therefore save significant resources from the French mammography screening program, with a “small but non-zero risk” of missed cancers.

The Takeaway
The new results track with findings from other recent studies that apply AI to mammography screening, particularly in Europe. While the French reading paradigm is unique, it’s instructive to see that AI maintains its ability to reduce radiologist workload across different types of breast cancer screening programs.

Does AI Still Scare Off Radiology Trainees?

Posted on May 27, 2026May 27, 2026 by Brian Casey

Is AI still scaring off medical students from picking radiology as a specialty? A new study in Academic Radiology found that while prospective radiology trainees don’t seem as worried as they were after radiology AI burst onto the scene in 2015, they still have concerns about how AI will affect the profession.

Radiology has long been seen as the medical specialty most at risk of broader AI adoption, largely because early AI applications focused primarily on image analysis.

These fears led to a widely publicized dip in radiology residency applications after 2016, the year after IBM Watson debuted at the RSNA show and when AI guru Geoffrey Hinton, PhD, issued his famous advice to stop training radiologists.

But interest in radiology rebounded shortly after that. AI adoption was slower than anticipated, and few hospitals have proven willing to turn over radiologists’ duties to computers.

Given the changes, how have the attitudes of medical students toward AI evolved in the last 10 years? Researchers decided to survey Canadian medical students and residents to find out.

In all, 401 respondents replied to the survey, of whom 13% had ranked radiology as their top specialty choice, with the following findings…

Only 2.5% said AI was “extremely influential” in affecting their specialty choice, with 57% saying it had a “slight/moderate impact” and 35% stated “no impact.”
AI was more important for those ranking radiology in their top three, with 91% saying AI influenced their decision compared to 54% of those uninterested in radiology.
For those interested in radiology, 33% said AI made them feel discouraged, 13% were encouraged, and 33% reported no AI influence.
Those who believed AI would reduce radiologist demand were 50% less likely to be interested in a radiology career.

How to interpret the results? The authors felt the findings showed that AI had either no influence or a slight/moderate effect on specialty choice, but the impact was greater in those who were interested in radiology.

They also saw a “growing polarization” among trainees, in that while many viewed AI as a threat to their job security, some saw it as an opportunity for innovation.

The Takeaway

Medical students have complex and nuanced attitudes toward AI in radiology, as the new study indicates. But the findings suggest that past fears of radiology AI have evolved into a more measured view that better reflects real-world AI adoption.

AI for PE Detection: ‘Selective but Meaningful’

Posted on May 13, 2026May 13, 2026 by Brian Casey

AI made a “selective but meaningful” contribution to radiologist interpretations of CT pulmonary angiography scans for pulmonary embolism. The study, published in Radiology: Artificial Intelligence, offers valuable insights into real-world implementation of AI on a large scale.

One of the major criticisms of AI is that algorithms used in real-world clinical situations don’t perform as well as they do in the controlled environments that vendors use to acquire data for regulatory submissions.

AI performance can drop off as much as 20 to 30 percentage points for important metrics like sensitivity and specificity.

The new study sought to investigate this phenomenon by analyzing a real-world implementation of Aidoc’s AI algorithm for PE detection.

Researchers assessed the algorithm’s performance for analyzing CTPA exams across a variety of clinical environments in an integrated health network, including the emergency department and inpatient and outpatient settings.

Scans of 29.5k patients acquired from 2021 to 2023 were included. AI analyzed images in real time, after which exams were interpreted by radiologists who knew the AI findings. Researchers found…

Radiologists using AI had higher sensitivity than the algorithm on its own (99% vs. 85%).
Specificity was more or less the same (99.8% vs. 99.5%).
Agreement between radiologists and AI was high (98%).
Agreement was higher when AI assessed cases as negative rather than positive (98% vs. 94%).
Radiologists disagreed with AI in 2.2% of cases. The final determination by a panel of expert thoracic radiologists strongly favored radiologists (89%).
Of the 3.3k cases positive for PE, 0.81% were detected only by AI – or 26 cases.

In analyzing the results, the researchers characterized AI’s contribution as “selective but meaningful.”

AI-positive results meant scans might require more scrutiny from radiologists, while an AI-negative call might be supportive – but not definitive – for negative PE.

The Takeaway

The new study of AI for PE detection is a fascinating look at real-world AI deployment. While the sensitivity, specificity, and agreement numbers are interesting, what draws our attention is the 26 PE cases caught only by AI over 18 months of use. That boils down to 26 patients whose clinical condition wasn’t missed, and 26 potential malpractice lawsuits that were never filed.

AI’s ROI Paradox

Posted on April 12, 2026April 12, 2026 by Brian Casey

As radiology AI slowly moves from pilot projects to widespread clinical adoption, a new survey reveals a paradox: The technology is popular with radiologists, but few imaging facilities using AI have collected hard data showing its return on investment.

AI’s slow clinical adoption has frustrated both clinicians and algorithm developers alike, but the technology is gaining steam.

Despite growing clinical evidence, research on AI’s financial value and ROI has been slower in coming.

To remedy that situation, AI governance startup Croviz.ai conducted a study of 445 radiology AI users on the economics and evaluation of AI. The full report is available here.

Survey respondents came from 12 different countries and included a variety of professional roles, including vendor executives, radiologists, and IT and informatics personnel.

Croviz founders Ayman Talkani and AadilMehdi Sanchawala found that while radiology AI power users loved the technology – and some refused to work without it – few had determined a positive financial return from it. Findings included…

95% of sites already using AI had renewed at least one contract with an AI vendor in the last 12 months.
But only 30% had quantified a positive financial ROI from AI.
54% cited better quality of life for radiologists as their main reason for renewing an AI contract.

So if AI’s value hasn’t been demonstrated, why are radiology sites renewing AI contracts?

The number one reason cited by 54% of those renewing contracts was because their radiologists felt AI improved their quality of life – the only outcome measure leadership could quickly measure with qualitative user feedback.
Lower on the scale was reduced turnaround time (18%), more scans per reader (10%), reduced downstream patient costs (10%), and better diagnostic accuracy (8%).
Just 6% paid attention to hard metrics like staff retention rates.

What’s the best way out of the AI ROI paradox? The Croviz researchers recommended more frequent and transparent AI governance.

Survey respondents who monitored AI performance more closely – such as more often than once per quarter – exhibited more trust in AI.

The Takeaway

The new survey offers an intriguing look at AI adoption and the question of ROI for the technology. It suggests that – much like another digital technology, PACS – AI adoption is being driven more by its popularity among radiologists than hard ROI considerations.

Mammo AI Momentum Builds

Posted on March 19, 2026March 18, 2026 by Brian Casey

Momentum is building toward routine clinical use of AI for breast cancer screening. Several new studies offer even more support for mammography AI, including research published today in Nature Medicine in which AI reduced radiologist workload by over 60% by excluding low-risk studies from human review.

Breast screening has become one of the most promising use cases for AI, with the potential to reduce radiologists’ workload while improving their ability to detect cancer.

For example, the recent MASAI study found that ScreenPoint Medical’s Transpara AI algorithm could replace the second human reader in a double-reading protocol, reducing workload by 44% and improving cancer detection rates by 28%.

The new research in Nature Medicine also used Transpara, as part of the AITIC study in Spain with the goal of seeing if AI could triage low-risk studies so they don’t require review by human radiologists.

AITIC had a prospective design, involving 31k women with screening exams split between 2D mammography (17k) and digital breast tomosynthesis (14k).

Women in the control arm of the study got conventional double reading by two radiologists – the standard mammography paradigm in Europe.

The intervention arm used a partially autonomous AI approach: cases that AI interpreted as low risk were classified as normal and were not reviewed by radiologists, while all other cases were double-read by radiologists using AI support.

In analyzing the results, researchers found…

Workload in the AI arm was 64% lower than conventional double reading.
AI’s workload reduction was similar between DBT and conventional digital mammography (-66% and -62%, respectively).
The AI arm’s cancer detection rate per 1k women was 15% higher (7.3 vs. 6.3 cancers).
But the recall rate was also 15% higher.

It’s worth noting that the AITIC study differed from MASAI in its inclusion of DBT screening exams, whereas MASAI only included 2D digital mammography.

While 2D mammography is the norm in Europe, much of the U.S. has switched to DBT for breast screening, so the AITIC results offer good news for U.S. breast imaging practices considering AI adoption.

The Takeaway

The AITIC study’s new results are powerful confirmation of findings from the recent MASAI trial and support broader clinical deployment of mammography AI. Taken together with positive findings from last week’s Nature Cancer articles (see The Wire section in this newsletter), they paint a picture of a technology that’s ready for prime time.

FDA AI Approvals Surge Past 1k for Radiology

Posted on December 10, 2025December 19, 2025 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.

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