The annual meeting of the Society for Imaging Informatics in Medicine convened in Portland, Oregon, with members of radiology’s imaging IT community joining together to discuss the latest trends in enterprise imaging, AI, and more.
As with other recent radiology meetings, AI dominated the discussion at SIIM 2025. But AI’s potential to revolutionize radiology has been tempered by nagging concerns about slow clinical adoption and questionable return on investment for healthcare providers.
Regulatory turbulence is also a concern, highlighted by recent changes implemented by the Trump Administration at the FDA. Some industry observers have speculated that AI approvals have slowed down, while others point out that the FDA – which has lagged other countries in approving new AI algorithms – perhaps might benefit from a fresh approach in how it regulates AI.
The Takeaway
In the end, SIIM 2025 can be chalked up as another success for the organization. While attendance seemed to be down slightly (most likely due to the West Coast location and pre-Memorial Day timing), the society pointed out that the number of vendor exhibitors at SIIM 2025 exceeded 100 for the first time in years – a sure sign of a healthy imaging IT industry.
Check out our SIIM 2025 videos below or visit the Shows page on our website, as well as our YouTube and LinkedIn pages, and keep an eye out for our next Imaging Wire newsletter on Thursday.
This week weary neuroradiologists descended upon the City of Brotherly Love for the annual meeting of the American Society of Neuroradiology (ASNR). The field is facing mounting pressure as increasing imaging volumes continue to outstrip radiologists’ capacity.
Dealing with growing volume was a recurring theme throughout ASNR 2025, with a range of proposed solutions, including the seven strategies below:
Acquisition automation for higher efficiency and reduced technical requirement: A talk by Lawrence Tanenbaum, MD, featured a number of AI solutions to ease technologist training requirements, including smart protocoling, automated patient positioning, one-touch exams without parameter adjustments, and on-device quality assurance and motion correction to cut down repeat exams.
Accelerated acquisitions as the standard-of-care: Every manufacturer – from established vendors to emerging startups – showcased deep learning-based reconstruction. As Suzie Bash, MD, put it, “Deep learning reconstruction is becoming standard-of-care across the industry.”
Improving radiologist reading efficiency with AI and workflow management: A noticeable trend at ASNR 2025 was fewer talks focused solely on algorithm accuracy and more emphasis on how AI impacts reading efficiency. Accuracy remains critical, but adoption increasingly hinges on demonstrating workflow efficiency.
Streamlining new algorithm rollout using integrated platforms: In a session on AI adoption and evaluation, Reza Forghani, MD, PhD, called for increased use of integrated platforms to allow for easier algorithm deployment, validation, and monitoring.
Rising reliance on international medical graduates (IMGs): Mina Hesami, MD, presented on the rising contribution of IMGs to US radiology, noting a steady increase in the proportion of residency slots, fellowships, and leadership roles held by international graduates – with radiology seeing faster growth than most other medical specialties.
Expanding the radiology workforce with mid-level providers: Another proposed strategy is offloading specific tasks to mid-level providers. While still controversial in radiology, this model is gaining traction in response to workforce shortages.
Sustainability by reducing emissions and environmental impact: Several ASNR sessions addressed environmental sustainability. From simply turning off idle scanners to using AI to reduce contrast doses, radiologists are beginning to reckon with the environmental impact of rising scan volumes.
The Takeaway
The sessions at ASNR 2025 indicate that while there’s a lot of buzz around AI, radiologists are considering every tool at their disposal to keep up with rising imaging volumes. AI will play a role, but likely won’t be sufficient alone to keep up with increasing volumes.
T. Campbell Arnold is a research scientist at Subtle Medical and the managing editor of RadAccess.
It’s no secret that radiology faces a variety of challenges, from rising imaging volumes to workforce shortages. But can imaging IT vendors help? A new paper in Academic Radiology suggests they can, and provides a list of the half-dozen imaging IT tools that radiologists say they need most.
Radiology is already one of the most software-oriented specialties in medicine.
It was an early adopter of digital healthcare through tools like PACS, and is reprising its leadership in the coming AI era with the lion’s share of FDA-approved medical AI applications.
But that doesn’t mean radiologists have all the IT tools at their disposal that they feel they need.
The new paper is a sort of radiologist wish list, developed after a 2024 meeting between vendors and members of the Association of Academic Radiologists.
Some three dozen key opinion leaders met for breakout discussions on radiology’s unmet IT needs. The discussion was then boiled down into six major areas …
Increased workstation efficiency, with better tools for looking through medical records to find clinical information.
Better AI tools for radiology reporting, such as auto-generated measurements and findings from prior studies for comparison.
Better methods for controlling imaging overutilization, such as clinical decision support systems to be used by referring physicians to order exams.
Help from vendors to improve access to high-level radiology services in underserved areas like rural communities, such as through industry-sponsored training positions or improved telemedicine access to patients with follow-up appointments.
Patient engagement tools that promote direct communication between radiologists and patients, including industry-sponsored training modules for radiologists to discuss findings with patients.
Simpler scheduling systems that allow patients to pick appointment times from their smartphones.
One possible question to ask about the recommendations is whether the needs of academic radiologists truly reflect those of radiologists in general, especially those in private practice.
But the items on the wish list appear broad enough that they hit the requirements of a wide range of imaging practitioners.
The Takeaway
Sure, radiologists face many challenges in today’s healthcare environment. But the fact that radiology is such an IT-centered specialty offers hope that new software tools can help them – and that radiology vendors can lend a hand.
Radiology venture capital funding appears to be gaining momentum in the first few weeks of 2025. This past week has seen the release of six funding rounds, led by a massive $260M Series B from preventive medicine firm Neko Health.
Venture capital funding is a closely watched barometer for any industry built on innovation, and radiology is no exception, especially the imaging AI sector.
Radiology venture capital funding got off to a particularly slow start in 2024, and by year end funding specifically for imaging AI was down 48% compared to 2023 ($335M vs. $646M according to Signify Research). This raised concerns about whether imaging startups might face a decline in VC investment – the equivalent of choking off their air supply before products in development could begin generating commercial sales.
This was widely seen as VC firms pivoting to quality, with investors demanding proof of progress in the clinical, regulatory, and commercial realms.
That brings us to the recent funding rounds …
Neko Health raised $260M that it will use to expand its Neko Body Scan from its current beachheads in Stockholm and London to other locations in Europe and the U.S.
Rad AI raised $60M in a round that follows on a $50M Series B less than a year ago as it moves to commercialize its AI-powered radiology reporting software.
Quibim raised $50M in a Series A round to advance its work in imaging biomarkers through AI foundation models that analyze MRI, CT, and PET scans.
Annalise.ai parent Harrison.ai received $20M (USD) in funding from an Australian government investment fund to further develop its radiology and pathology AI.
Springbok Analytics raised $5M in a Series A round to fund its AI technology for analyzing muscle health from MRI scans.
Sycai Medical raised $3.1M for its AI for detecting abdominal cancers.
Do this week’s developments in radiology venture capital funding represent a boom, a boomlet, or just a string of coincidences? Whichever it is, startups would have to acknowledge that any interest from VC investors is better than the alternative.
A new study lays the groundwork for AI-based opportunistic screening – the detection of disease using medical images acquired for other indications. In a paper in AJR, researchers show how their homegrown AI algorithm was able to analyze abdominal CT scans and link body composition measurements to the presence of disease.
Opportunistic screening is a sort of holy grail for radiology, with the potential to help radiologists find pathology from scans ordered for other clinical indications.
Some researchers specifically are focusing on analysis of body composition characteristics derived from CT scans like muscle, fat, and bone that could be biomarkers for hidden pathology – and AI is key because it can process mountains of patient data without getting tired.
In the new paper, researchers from the NIH and the University of Wisconsin tested the concept of AI-based body composition analysis on a massive database of 118k patients who got abdominal CT scans from 2000 to 2021.
They analyzed the scans with their own internally developed AI tool that measures 13 features of body composition, from volume and attenuation in different organs to area of subcutaneous adipose tissue.
Their goal was to correlate the AI measurements with actual presence of disease, as well as other factors that could affect body composition like age and sex. They found …
AI-based body composition metrics varied by age and sex, confirming previous studies.
AI metrics also correlated with the four systemic diseases studied, specifically cancer, cardiovascular disease, diabetes mellitus, and cirrhosis.
The predictive power of different metrics varied by disease, from a high of 13 measures for diabetes to a low of nine for cancer.
What’s the real-world impact of the study?
In addition to validating the concept of AI-based opportunistic screening on a broad scale, the findings could be used to establish a set of normal values for body composition that also take into account the impact of systemic disease on these measurements.
The Takeaway
The new study is a bit technical, but it’s an important milestone on the path to opportunistic screening. It not only demonstrates the concept’s feasibility, but also begins to establish the normal values needed to actually implement screening programs in the real world.
Body composition plays a pivotal role in monitoring organ and tissue health and predicting treatment outcomes. Accurate changes in body composition metrics can indicate reduced muscle quantity and quality – a sign of sarcopenia – as well as altered fat distribution in organs such as the liver in metabolic diseases, epicardial and paracardial fats in cardiovascular health, and more.
However, manual segmentation is time-consuming and labor-intensive.
Voronoi Health Analytics eliminates this bottleneck by combining cutting-edge AI with efficient visualization tools, automating the extraction of body composition metrics from CT and MRI scans. The company’s solutions transform imaging data into actionable insights, improving patient outcomes.
Voronoi Health Analytics provides innovative, intuitive AI tools that enable clinicians and researchers to extract quantitative body composition measurements rapidly and with high accuracy – no programming required.
The company’s platforms are trusted by over 175 research labs across 25 countries, with numerous publications validating their accuracy and impact on clinical care and medical research.
Voronoi has two flagship solutions …
DAFS: A comprehensive 3D segmentation platform for analyzing multiple tissues, organs, lesions, and vasculature across CT and PET/CT imaging. DAFS also overlays CT segmentations onto PET scans, enabling rapid, high-accuracy assessments of PET tracer uptake in organs, tissues, and lesions.
DAFS Express: Optimized for single-slice body composition analysis from CT and MRI scans, this tool delivers precise measurements of skeletal muscle, visceral fat, intermuscular fat, and subcutaneous fat in seconds, making it ideal for high-throughput clinical settings.
Accurate body composition analysis is critical for staging body habitus, detecting onset of signatures of adverse health such as metabolic or cardiovascular disorders, evaluating disease progression, and monitoring organ and tissue health as a function of disease and intervention. Voronoi’s platforms address key challenges such as …
Reducing Workloads: Automate routine segmentation tasks and allow clinicians to focus on complex cases.
Improving Precision: Deliver consistent, reproducible results across patients and studies.
Advancing Care: Provide predictive insights that help optimize treatment strategies.
DAFS and DAFS Express seamlessly integrate into existing imaging workflows, enhancing efficiency without disrupting operations.
Body composition analysis goes beyond measuring muscle and fat. It quantifies all organs and tissues, creating data that drives predictive models.
Voronoi’s vision is to empower healthcare professionals with tools that simplify complexity, support proactive care, and enhance patient outcomes.
Discover how Voronoi Health Analytics is revolutionizing body composition analysis. Visit the company’s website to request a demo and elevate your workflow today.
Radiology departments, imaging centers, and radiology practices are facing a perfect storm of challenges to deliver high-quality patient care while remaining profitable and competitive.
This familiar narrative emphasizes the need for change and to embrace automation, AI, and technology solutions that automate routine tasks.
RADIN Health has developed an innovative, cloud-based (SaaS), all-in-one technology stack based on the firsthand experience of radiologist Alejandro Bugnone, MD, CEO and medical director of Total Medical Imaging (TMI), a teleradiology group that reads for imaging centers and hospital systems nationally.
Dr. Bugnone and his team of radiologists were similarly suffering from supply and demand imbalance, reimbursement cuts, increasing study volumes, and customer pressures to maintain their margin.
As a software developer and seasoned radiologist, Dr. Bugnone was equally frustrated by the lack of a comprehensive, end-to-end technology solution in the market to address these same issues for his teleradiology practice.
In evaluating numerous RIS, PACS, AI voice recognition, and workflow management solutions, his team found that each required expensive interfaces, separate company fees, and ongoing support, yet as an ecosystem still did not deliver a seamless experience that would provide a return on investment.
An alternative is a system based on straight-through processing, a concept first pioneered in the financial services industry in which automation electronically processes transactions without manual intervention.
“I knew there had to be a better way forward. I founded RADIN Health for healthcare and teleradiology practices [like TMI], imaging centers, and radiology departments based on straight-through processing, similar to how Wall Street sped up financial transactions without any human intervention,” Dr. Bugnone said.
RADIN Health is a cloud-based platform that combines RIS, PACS, dictation AI, and workflow management into an all-in-one software solution.
It leverages artificial intelligence, machine learning, OCR/AI, natural language processing (NLP), and other intellectual property.
Dr. Bugnone said TMI has achieved remarkable efficiencies with RADIN.
“Our results at TMI have been staggering since implementing RADIN over the past 18 months for our complex teleradiology practice,” Dr. Bugnone noted. “With RADIN DICTATION AI, our radiologists have increased their productivity and efficiency, reducing dictation times 30% to 50%.”
By adding RADIN SELECT, TMI reduced its SLAs more than 50% and FTEs by 70% for managing operational workflow tasks, all while adding 35% in study volumes.
RADIN’s all-in-one technology solution has enabled Total Medical Imaging to meet the challenges of the radiology crisis without hiring new personnel – simply by unlocking the efficiency of their existing staff.
“We have enjoyed significant growth in 2024 without the need to hire additional staff,” Dr. Bugnone concluded.
Watch the video below to see how RADIN’s all-in-one solution can help your practice.
Repetitive, arduous tasks are a major contributor to burnout – an increasingly prevalent issue in healthcare. While digital innovation is transformative, introducing more technology to workflows often creates additional layers of complexity, hindering efficiency, performance monitoring, and ultimately the quality of care.
As a result, once-simple traditional workflows have grown cumbersome over time, filled with many interconnected tasks that are difficult to manage.
As these processes become more complex, it’s clear that healthcare needs to reduce, subtract, and simplify to maintain high standards of care.
Every traditional (or macro) workflow consists of multiple smaller tasks or steps (micro-workflows), many of which are still performed manually.
Consider a wound care scenario where a practitioner takes images, searches for the patient’s record in the EHR, uploads the images, and manually enters encounter details.
While each individual task may seem small, when multiplied by dozens of similar interactions each day, these repetitive steps …
Decrease the time providers have for meaningful patient interactions.
Lower overall productivity.
Increase the potential for human error.
Contribute to burnout and fatigue.
Micro-workflows address this by breaking down processes into discrete, manageable steps. For example, by …
Identifying the patient within the EHR.
Capturing the image.
Automatically inputting relevant metadata.
Seamlessly sharing the image with the care team.
This granular approach enables automation, allowing individual components to be optimized or modified without disrupting the entire process.
Micro-workflows offer adaptability, efficiency, and responsiveness, meeting evolving clinical requirements while reducing complexity.
Moreover, micro-workflows make it possible to monitor individual tasks with precision.
This approach allows healthcare organizations to pinpoint workflow gaps, troubleshoot issues, and resolve performance bottlenecks.
In multi-vendor environments, where integrating various systems and applications can be a challenge, the ability to streamline processes and automate tasks becomes especially valuable.
Strings by Paragon is a platform specifically designed to help healthcare organizations harness the power of micro-workflows.
By breaking traditional workflows into smaller, more manageable steps, Strings enables automation, real-time performance tracking, and monitoring across a wide range of applications and infrastructure.
The platform’s single-pane-of-glass interface provides visibility into complex, multi-vendor environments.
Strings offers actionable insights and automated optimizations tailored to specific clinical workflows.
With Strings, organizations can proactively identify workflow bottlenecks, implement targeted optimizations, and measure performance and ROI with precision – leading to improved efficiency, enhanced imaging quality, better patient outcomes, and a value-driven approach to care.
Learn more about Strings by visiting Paragon Health IT’s website, or visit them at RSNA 2024 at booth #1849.
At first glance, it may appear having more patients should naturally lead to higher revenue. When you consider extra labor costs and the fact that reimbursements are decreasing, increased volume can turn into diminishing returns.
Basically, the cost of adding more staff can end up being higher than the value of additional patient volumes.
Optimal management of growing patient volumes requires a new way of working with automation and cloud-based apps that replace the heavy burden of manual processes.
By using technology to eliminate processes, medical facilities manage patient loads better without the need for more labor costs.
This proactive approach not only improves efficiencies but also lets front office staff focus on patient needs instead of getting bogged down with administrative tasks.
Ultimately, shifting towards automation and consolidation of tasks is key to maintaining clinic profitability and keeping high standards of care, especially with increasing medical demands.
How RamSoft Can Help Simplify Front Office Operations
Achieving workflow excellence starts with a single sign-on into a unified RIS/PACS and providing access to complementary medical imaging apps via a single worklist in the cloud.
By leveraging cloud applications with scalability across facilities, organizations can “build as they grow,” while maintaining control and flexibility.
Blume – Patient Portal: Patient access to diagnostic images and reports, imaging sharing with referring clinicians and family, self-scheduling, intake forms, and appointment notifications. These self-service features decrease the number of phone calls, the time needed for patient registration, and the manual process of intake form completion and filing.
pVerify: Batch verification and real-time eligibility (authorization available soon) eliminates the need to call multiple insurance providers, freeing up staff time while reducing denials.
PracticeSuite: An embedded solution including workflow options to accommodate entries from the RIS/PACS worklist or within the billing module. Quickly accesses top billing functions, Payment Ledger for balances and eligibility, and Payment Entry to add payment and print a receipt.
openDoctor: Automated appointment notifications through SMS and email which replaces lists of confirmation calls and reduces missed appointments.
InterFAX by Upland: Integrated digital workflow for inbound (available soon) and outbound faxes, reducing the need for manual acceptance and processing of referral or report faxes.
Mobile Applications Are Building a Patient-Centric Experience
Protecting patient data is business-critical for all medical practices, as it is for RamSoft. We’re using Microsoft Azure Cloud to ensure all data and applications are secure.
Workflow optimization in medical imaging can significantly impact the patient experience, leading to increased loyalty and satisfaction.
Is Your Practice Operating Optimally?
Explore how RamSoft’s new automation applications, including patient engagement tools, integrated with cloud-based RIS/PACS can improve operations and profitability of your practice.
The U.K. is planning a massive project – worth close to $1B – to procure new IT tools for medical diagnostic use. While details of the plan are still sketchy, it involves the acquisition of both radiology and cardiology PACS, as well as AI.
The U.K.’s NHS has become one of the world’s hottest test beds for medical IT adoption as the service struggles to reconcile a static workforce with rising demand for healthcare services.
For example, the NHS last year issued the AI Diagnostic Fund, which provided £21 million ($28M) for a variety of AI implementation projects across 64 NHS trusts.
But the new tender offer dwarfs that investment. NHS has proposed a Digital Diagnostic Solutions project to serve as “a route to market for departmental wide diagnostic IT solutions.”
The value of the project is pegged at £700M ($923M), a massive investment in medical IT by any metric.
The offer is being led by NHS Supply Chain, the governmental agency responsible for procuring medical equipment within the NHS.
The program’s tender offer states that the Digital Diagnostic Solutions project “is to be the new Framework for the Medical IT Departmental Software and Hardware Solutions framework within NHS Supply Chain.”
It includes the following provisions:
Acquisition of radiology PACS, cardiology PACS, RIS, cardiovascular information systems (CVIS), laboratory information management systems (LIMS), and vendor-neutral archives (VNAs).
Software acquired through the program “will sit alongside” other capital equipment like X-ray, MRI, and CT systems.
It will also include 3D software, diagnostic AI software, and endoscopy image management applications.
Publication of an invitation to tender will happen in December 2024, and the contract award will be in July 2025, with the framework itself starting in August 2025.
The tender offer was published just a few days before a government-commissioned report that said the NHS was in “serious trouble” and that was harshly critical of the system’s transformation to digital operation.
And that report came after a July election that saw the Labour party win power for the first time in 14 years – raising hopes that it would approach NHS funding differently than the previous Conservative governments.
The Takeaway
Does the Digital Diagnostic Solutions project represent a new commitment to funding IT innovation from the Labour government? Or is it simply a rebranding of the NHS’ existing procurement activities? Stay tuned.
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