Tag: AI

AI for PE Detection: ‘Selective but Meaningful’

Posted on 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.

Mammo AI Momentum Builds

Posted on 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 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 Quantib 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.

Agentic AI for Radiology Follow-Up

Posted on by Brian Casey

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

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

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

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

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

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

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

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

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

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

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

The Takeaway

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

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.

Canon Celebrates 50 Years of CT Innovation: Redefining Healthcare with Meaningful AI

Posted on by Partner Content

This year marks a historic milestone for Canon – five decades of pioneering CT innovation that has transformed the landscape of healthcare. From introducing industry-first technologies to setting new standards in diagnostic imaging, Canon continues to lead the way in delivering solutions that matter.

Canon’s legacy is built on breakthroughs such as its three-time award-winning wide-area CT systems, deep learning reconstruction that brings 1K resolution to CT imaging, and automation improving workflow. 

  • These innovations have consistently elevated diagnostic confidence, patient safety, and operational efficiency.

In today’s world, AI is everywhere – but Canon’s AI is Meaningful AI. It’s not about AI for the sake of technology; it’s about creating real-world impact on patient care. 

  • Canon’s portfolio of scanner-integrated AI applications is designed to enhance image quality, streamline workflows, and improve consistency – ultimately delivering better care, better experience, and better efficiency for patients and providers alike.

Canon is redefining CT by making AI a core component across its portfolio. Key innovations include…

  • AI-Assisted Scanner Workflow Automation. Canon’s INSTINX platform introduces intuitive, intelligent, and integrated AI technologies that enable autonomous CT operations. By simplifying complex workflows, INSTINX helps technologists focus on patient care while improving throughput and reducing variability.
  • AI-Assisted Post-Processing. Canon’s Automation Platform offers a zero-click, AI-driven solution that accelerates image post-processing. By delivering fast, actionable insights, this platform ensures time-critical results reach care teams when they need them most.
  • AI-Assisted Reconstruction. Advanced algorithms such as AiCE DLR and PIQE DLR leverage deep learning to reveal critical diagnostic information – contrast and resolution – while optimizing dose efficiency. These tools empower clinicians to make confident diagnoses and reduce the need for additional downstream studies. Additionally, CLEARMotion, a DCNN-based algorithm, compensates for patient motion, reducing blur and delivering high-quality results even in challenging cases.

The Takeaway 

As Canon celebrates 50 years of CT innovation, its commitment remains clear: harnessing AI to make imaging smarter, faster, and more meaningful. With these advancements, Canon is not just shaping the future of CT – it’s setting a new benchmark for patient-centered care.

Next-Generation AI Platform Redefines Radiology Workflow Standards

Posted on by Partner Content

AI is no longer being viewed as a diagnostic aid but as essential medical infrastructure. Nowhere is that more apparent than in lung screening, with Germany and other European Union countries increasingly embedding AI into their lung cancer screening guidelines and pilot programs.

This evolution will be on display at RSNA 2025, where Coreline Soft will introduce its groundbreaking chest AI platform AVIEW 2.0.

  • The solution demonstrates how unified AI automation is fundamentally transforming radiology workflows and elevating diagnostic precision across pulmonary, cardiac, and airway pathologies.

AVIEW 2.0 represents a paradigm shift from task-specific tools to an integrated diagnostic ecosystem. 

  • The platform seamlessly combines lung-cancer screening (LCS), coronary-artery calcium (CAC) scoring, and COPD quantification into a single, continuous analytical pipeline. 

Clinical validation shows radiologists using AVIEW 2.0 achieve 89% increase in case throughput and 60% reduction in interpretation time compared to the previous generation. 

  • This effectively consolidates multi-disease CT assessment into one streamlined, automated workflow.

AVIEW’s clinical foundation extends far beyond pilot studies. The platform has processed over 2.5M cases across 19 countries, establishing itself as a proven solution in diverse healthcare ecosystems. 

  • Most notably, AVIEW has been selected as the AI platform for major government-led lung cancer screening pilots and programs in Germany, France, and Italy.

Beyond Europe, AVIEW solutions are already integrated into major U.S. medical centers, where their clinical reliability has been independently validated in real-world settings…

  • UMass Memorial Medical Center has deployed the system as an integrated platform for LCS, CAC, and COPD diagnosis, supporting full-spectrum thoracic screening in daily radiology operations.
  • Temple Lung Center, 3DR Labs, and ImageCare Radiology have incorporated AVIEW products into their research and diagnostic environments – each adapting AI functions to site-specific workflows and physician preferences.

SOL Radiology, a fast-growing radiologist-owned practice serving communities across California and Illinois, has deployed AVIEW LCS Plus across its outpatient centers and hospital network, leveraging the platform for high-confidence nodule detection, rapid turnaround, and integrated COPD/CAC assessment. 

  • The group reports significant gains in diagnostic efficiency and consistency within one week of implementation, supporting its vision for technology-driven, high-quality community radiology.

With national-scale validation in Europe, clinical adoption across top-tier U.S. institutions, and 2.5M cases processed globally, Coreline Soft is positioning AVIEW 2.0 as the new benchmark for AI-driven thoracic imaging – where efficiency, accuracy, and scalability converge.

The Takeaway

Coreline Soft will conduct an end-to-end AI workflow demonstration in the “Radiology Reimagined” demo zone at RSNA 2025, using real-world clinical scenarios. With AVIEW and HUB, the full pathway – from triage and interpretation to reporting and quality management – will be validated against standards such as IHE and FHIR, allowing attendees to experience integrated flow firsthand. Learn more or book an appointment on Coreline Soft’s website.

RP Acquires Vision AI Firm Cognita Imaging

Posted on by Brian Casey

Radiology Partners ramped up its investment in AI by acquiring Cognita Imaging, a startup that’s developed AI vision language models for analyzing CT and X-ray images and drafting initial radiology reports. RP executives see the acquisition as going beyond traditional point-source AI models and toward a future where AI automates much of the traditional image interpretation process.

The $80M acquisition expands on an equity stake RP already had in Cognita, which had been operating in stealth mode since its spin-off from Stanford University’s Center for Artificial Intelligence in Medicine and Imaging lab.

  • Cognita was formed by a team led by CEO Louis Blankemeier, PhD, to commercialize Stanford research on vision language models, a type of generative AI that’s far more versatile than the traditional point-source models being commercialized to analyze medical images.

Instead, Cognita’s technology is able to analyze text as well as CT or X-ray images and produce first drafts of radiology reports that just need a radiologist’s review and signature to be complete.

  • Extremely positive clinical tests with Cognita’s VLM models spurred RP to acquire the rest of the company it didn’t already own, said Rich Whitney, chairman and CEO of Radiology Partners. 

Cognita’s technology powers Mosaic Drafting, RP’s new application for helping radiologists draft reports that operates under the company’s recently launched Mosaic Clinical Technologies branding. Early clinical testing has found that Mosaic Drafting…

  • Increases radiologist detection rates by 52%.
  • Results in a fourfold decline in radiologist errors.
  • Reduces radiologist reading times by up to 76%.

RP plans to deploy Mosaic Drafting through Mosaic Clinical Technologies, which the company launched in July as the technological foundation for a massive rollout of AI across its physician practices. 

  • Mosaic Chief Medical AI Officer Nina Kottler, MD, said Mosaic Drafting is currently being used within Radiology Partners under IRB approval, but the company will pursue an FDA authorization – most likely under a de novo pathway – that probably will come sometime in 2026.

In a broader sense, RP sees Mosaic Drafting and other VLM tools as key to the growing mismatch between rising imaging volume and stagnant radiologist supply – a mismatch that can only be solved through greater automation. 

  • And as the largest private radiology organization in the U.S., Radiology Partners has the organizational heft to make VLMs work on a wide scale.

The Takeaway 

RP’s acquisition of Cognita is a major development in putting vision language models on the fast track to real-world clinical use. Unlike point-source AI, VLMs could hold the key to really solving radiology’s volume overload dilemma.

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. 

AI in Radiology: Old Problems, New Tech

Posted on by Partner Content

By Mo Abdolell, CEO, Densitas

Radiology has seen this movie before. Big promises (efficiency, accuracy, burnout relief). Big anxieties (ROI, workflow chaos, pressure to “keep up”). The question isn’t whether AI is powerful. It’s whether we’ve learned how to deploy new technology without repeating the pain of PACS migrations and the EHR era.

The Myth of the Perfect Rollout. Health technology assessment (HTA) sounds great in theory – rigorous, comprehensive, evidence-first. In practice, few organizations have the time, talent, or budget to execute it at scale. 

  • Remember EHRs: adoption happened because policy and money forced it, not because the playbook was tidy. Healthcare’s default pattern is to adopt, then evolve – messy, market-driven, and iterative. Waiting for perfect plans is how you get left behind.

Are AI’s Problems really new?

  • Black box déjà vu. Radiology has long trusted complex, opaque systems (reconstruction algorithms, vendor-specific pipelines). What mattered – and still matters – is validated performance and dependable outputs, not full internal transparency.
  • Model drift ≈ old friends. We’ve always recalibrated clinical tools as populations and scanners change. Monitoring and revalidation are known problems, not alien ones.

What’s Different This Time? Unlike the top-down EHR mandate, AI is largely market-driven. That gives providers agency. 

  • AI solutions must save time, improve outcomes, or avoid costs – not just publish a ROC curve. They must show operational value inside the native radiology workflow.

Fortunately, there are ways to adopt AI and then evolve your processes to make it work…

  • Workflow or bust. Demand in-viewer evidence objects, one-click report insertion, and EHR write-back. If AI adds steps, it subtracts value.
  • Start narrow, scale deliberately. Pick high-volume, high-friction tasks. Prove value in weeks, not years. Expand only when the operational signal is undeniable.
  • Measure what matters. Track operational metrics like seconds saved and coverage (e.g. eligible cases processed before dictation), reliability (e.g. results present before finalization, fail-open behavior), and user friction like context-switching rate and time-to-evidence.
  • Monitor. Stand up organization and site-level performance checks. Treat AI like equipment – scheduled, observed, and maintained.
  • Invest in long-term value. Favor standards, vendor-agnostic interoperability, clear telemetry, and transparent pricing.

The Takeaway

AI’s success in radiology won’t be defined by elegance of algorithms but by pragmatism of deployment. This will be an evolution – hands-on, incremental, sometimes messy. The difference now is that radiology can drive. Make the technology serve the service line – not the other way around.

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