Tag: imaging AI

Michael Mardini’s New Startup

Posted on by Brian Casey

The imaging informatics entrepreneur who helped drive radiology’s transition to speech recognition is returning with a new start-up. Michael Mardini is bringing Vware.ai out of stealth to commercialize technology created at Mass General Brigham for developing and deploying agentic AI. 

Mardini’s roots in radiology go back decades, and he’s been involved in at least three startups that had successful exits…

  • Talk Technology, an early developer of speech recognition and workflow applications acquired by Agfa HealthCare in 2001.
  • Commissure, a radiology reporting developer acquired by Nuance in 2007. Nuance used Commissure’s RadWhere as the foundation for PowerScribe 360, which came under Microsoft’s umbrella with its 2022 purchase of Nuance. 
  • National Decision Support Company (NDSC), a developer of clinical decision support software acquired by Change Healthcare in 2018. 

All of Mardini’s ventures have focused on improving radiologist productivity, which remains an issue given rising imaging volume and a stagnant workforce.

  • Speech recognition-based reporting created a leap in radiologist productivity, and Mardini sees a similar jump possible with agentic AI, especially as many healthcare providers are creating their own AI agents.

But developing and deploying AI agents often involves significant IT complexity. And that’s where Vware comes in. 

  • Vware has taken technology developed at Mass General Brigham’s MGB AI division under the purview of Keith Dreyer, DO, PhD, and is adapting it for the commercial market as Composer IQ. Mardini and Dreyer discussed the new venture in an interview with The Imaging Wire.

Dreyer likens the technology behind Composer IQ to an agentic AI builder and orchestration platform that not only hosts AI agents but also enables subject-matter experts – like radiologists – with no programming experience to develop agents for their own use. 

  • There are already hundreds of agents running within Composer IQ, including Case Engine, which loads relevant patient data radiologists need for interpreting images. Other agents are available for tasks like reading coronary artery calcium scans, billing optimization, and case reporting.

Vware plans to offer Composer IQ directly to radiology facilities as well as through partnerships with PACS and AI companies. The firm will be discussing the technology at the upcoming RSNA 2026 (although it won’t be exhibiting).

The Takeaway

Will lightning strike a fourth time for Michael Mardini and Vware? Only time will tell, but there’s no question that by focusing on agentic AI, he’s once again targeted one of the hottest areas of radiology for his latest entrepreneurial venture. 

Blackford Finds Buyer with Sopris Acquisition

Posted on 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. 

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 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. 

Harrison’s Teleradiology Play

Posted on 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. 

Your Reporting Platform Is Now Your AI Strategy

Posted on by Partner Content

By Sheela Agarwal, MD, MBA, a practicing radiologist and chief medical information officer at Microsoft 

For most of my career, the radiology reporting platform was a workflow tool. Today, it is an AI strategy, and the way we evaluate it must change with it. 

I see that from two sides. As a practicing radiologist, I live in the reporting workflow. As CMIO at Microsoft, I help shape how technology serves it. 

  • The platform underlying radiology reporting now determines something bigger than features. It determines how effectively AI is integrated into the workflow and where it is making the biggest impact, for both radiologists and the care patients receive.  

The pressure is real, and it is human. Imaging volumes keep climbing while the workforce to read images struggles to keep pace, and the cognitive load on every radiologist grows with it. 

  • The question is no longer whether to adopt AI. It is which foundation can carry AI reliably, study after study. 

Most reporting solutions were not built to be that foundation. PowerScribe One is. The proof is in the scale: 

  • 280+ organizations, proven across IDNs, academic centers, community hospitals, and independent practices. 
  • 10,000+ radiologists have made PowerScribe One part of how they work. 
  • 10M+ reports every month, a volume that reflects real, sustained use. 

That scale is not the point on its own. It is evidence that the technology delivers where it counts. What matters to a radiologist is more specific: does it lighten a heavy worklist, or just add another click? 

As an AI companion to PowerScribe One, Dragon Copilot builds on that foundation, bringing prior reports, patient context, and information from credible sources to the same screen as they read, so radiologists work with a more complete picture without leaving their workflow. 

  • The deeper value is what it frees radiologists to do: practice at the top of their license, spending less time on the mechanics of report creation and more on the complex reads and the diagnostic judgment that shape a patient’s care. 

A reporting platform must be ready for what comes next, evolving alongside radiology and AI. 

  • For organizations still on PowerScribe 360, now is the time to evaluate the path forward. Migration to PowerScribe One preserves existing workflows and configurations, allowing organizations to modernize at their own pace without disrupting care. 

The Takeaway 

This is not simply a radiology reporting system upgrade. It is choosing a partner committed to practical innovation, built on decades of workflow expertise and deep collaboration with customers and partners. Connect with Microsoft’s team or click here to learn more about PowerScribe One and Dragon Copilot.

SIIM 2026 Video Highlights

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine is always one of the highlights on the radiology calendar. SIIM 2026 was no exception, once again underscoring the vibrant community driving advances in imaging IT.

From radiology reporting to enterprise image management, SIIM 2026 highlighted the state of the art in imaging IT. We talked to many of radiology IT’s key opinion leaders in Pittsburgh, and we’re pleased to bring the discussions to you in this newsletter.

We hope you enjoy watching our SIIM 2026 video coverage as much as we enjoyed producing it! 

Check out the SIIM 2026 video links below or visit the Shows page on our website, and keep an eye out for our next Imaging Wire newsletter on Thursday.

– Brian Casey, Managing Editor

Top Trends from SIIM 2026

Posted on by Brian Casey

Last week’s SIIM 2026 conference demonstrated once again radiology’s ongoing evolution, from a discipline once known for big iron to one dominated by software. From radiology reporting to the evolving AI platform segment, below are the top seven trends from Pittsburgh. 

  • Reporting Stays Red Hot: Radiology reporting was the top theme from SIIM 2025, and the segment got even hotter with Microsoft’s decision to sunset its PowerScribe 360 radiology reporting software, which has drawn a host of new competitors into the segment. At SIIM 2026, a common theme was enterprise imaging companies adding reporting modules to their solutions.  
  • AI Adoption Moving Slowly But Surely: Adoption of radiology AI has been frustratingly slow, but it’s moving inexorably toward broader clinical use. At SIIM 2026, some 68% of the radiology-oriented papers focused on AI in some way, especially the new generation of foundation and vision language models that are enabling targeted AI algorithms to be developed more quickly than ever.
  • AI Governance Gets Real: Growing adoption of AI algorithms is creating a new issue: How to manage all this new technology. AI governance therefore was a major issue at SIIM 2026 as healthcare providers debated the legal and ethical necessity to better manage AI adoption, deployment, and utilization.
  • Other ‘Ologies Get into the Act: Radiology likes to think of SIIM as its own conference, but it also encompasses other ‘ologies that are moving into digital image management, like pathology and ophthalmology. At SIIM 2026, several imaging IT vendors showed integration with data from these disciplines, giving healthcare institutions a single source for their healthcare data management.
  • The Rise of All-in-One Vendors: A growing number of imaging IT vendors are rolling out solutions that combine image viewer, worklist, and reporting into a single platform, simplifying purchasing, deployment, and maintenance for radiology customers. Many of these firms seem to be getting traction with potential buyers, indicating the all-in-one concept could be one whose time has come.
  • Agentic AI Takes Shape: Agentic AI is a growing trend in radiology as algorithm developers build solutions to take on mundane tasks and free up radiologists to focus on their primary task: interpreting images. But the question is, will agentic AI work in the real world, or simply pile more technology on clinicians?
  • What Next for AI Platforms? Bayer’s withdrawal from the AI platform market by pulling its support for Blackford in 2025 raised many questions about the platform model that persisted at SIIM 2026. AI platforms seem to be evolving to add additional services like AI monitoring and governance.

The Takeaway

SIIM may not be radiology’s largest show, but for those in the imaging IT space it may be the most valuable one outside of RSNA. SIIM 2026 proved that point, with the top trends from Pittsburgh illustrating the discipline’s direction at the midpoint of the radiology year. For our overview of the top trends at SIIM 2026, check out our YouTube channel or the Shows tab on our webpage.

AI Reduces Mammography Workload

Posted on 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.

AI’s ROI Paradox

Posted on 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.

Doctors Adopt ‘Shadow AI’ for Efficiency Gains

Posted on by Brian Casey

Doctors under pressure to work more efficiently are looking for help from “shadow AI” – artificial intelligence applications adopted outside a formal hospital approval process. A new survey of U.S. healthcare personnel found that many administrators have encountered unauthorized AI tools in their organizations, including some used for direct patient care. 

U.S. healthcare providers are struggling under rising patient volumes in the midst of an ongoing workforce shortage, a situation that’s leading to burnout among clinicians. 

  • AI is often touted as a possible solution by enabling providers to do more with less, but the jury is still out on whether this works in the real world. 

The new survey was conducted by Wolters Kluwer Health to assess usage of what the report described as “shadow AI,” or AI that’s adopted without proper hospital authorization processes. 

  • Shadow AI introduces risk to data, security, and privacy, and providers should better understand the need for an enterprise approach to AI with appropriate controls.

It’s worth noting that the report’s use of the term “authorization” applies primarily to an institution’s internal approval and governance processes for AI rather than formal FDA regulatory authorization. 

  • AI algorithms that aren’t used for direct patient care don’t require FDA authorization, as the agency pointed out in a guidance just a few weeks ago. 

Researchers surveyed 518 health professionals, finding…

  • 41% were aware of colleagues using unauthorized AI tools.
  • 17% said they had personally used an unauthorized tool.
  • 10% said they had used an unauthorized AI tool for direct patient care.

While the report’s recommendation for stronger AI governance is valid, there could be a competitive subtext to the findings. Wolters Kluwer offers healthcare clinical decision support solutions, and the company is currently locked in a fierce battle with OpenEvidence for dominance in the CDS space.

  • OpenEvidence’s CDS solution is wildly popular with clinicians, many of whom install and consult with the software on their own, outside an enterprise-level governance – exactly the kind of “unauthorized” model the new report criticizes.

The Takeaway

The Wolters Kluwer report could be shedding light on a concerning new trend, or it could represent an effort by an established player to shut out a competitive threat. Either way, its warning on the need for appropriate enterprise-level AI governance should not be ignored.

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