Category: Imaging IT

GE to Buy Intelerad in Massive $2.3B Acquisition

Posted on by Brian Casey

In what could be the biggest radiology IT acquisition in years, GE HealthCare will acquire medical image management software company Intelerad in a purchase valued at $2.3B. The acquisition will bolster GE’s position in the outpatient image management segment, which is rapidly shifting from on-premises PACS models to cloud-based environments.

Intelerad was founded in Montreal in 1999 as a PACS developer and has grown through acquisitions of its own in recent years.

  • U.K. private equity firm Hg took a controlling interest in Intelerad in 2020, and the company soon embarked on a series of acquisitions that rolled up smaller imaging IT companies like Digisonics (2020), Ambra Health (2021), Insignia (2021), Lumedx (2021), Life Image (2022), and PenRad Technologies (2022). 

After taking a few years to digest the new companies, Intelerad began focusing on moving its technology and customers to cloud-based architecture, such as by releasing a cloud-native version of its InteleHeart software and by moving its PACS, VNA, and image-sharing applications to AWS cloud hosting.

GE needs no introduction, of course, but the company clearly sees the attraction of Intelerad’s core market in outpatient imaging, which complements GE’s focus on larger hospitals and health systems. 

In a conversation with The Imaging Wire, Scott Miller, president and CEO, Solutions for Enterprise Imaging at GE HealthCare, explained several of the acquisition’s advantages …

  • Imaging exams are moving from hospitals to outpatient centers due to lower costs.
  • Outpatient facilities are following hospitals in moving their data to the cloud, putting Intelerad at the intersection of two major trends.
  • Intelerad’s geographic focus has been on English-speaking countries, giving GE the opportunity to plug Intelerad products into its international distribution network. 

GE estimates that Intelerad will generate $270M in revenue in its first full year under GE ownership. 

  • Intelerad’s sales have been growing at a rate in the low double digits, and GE expects that pace to accelerate. 

Is the new acquisition a sign of growing consolidation in the radiology AI and image management sectors? 

  • Other recent purchases in 2025 include Radiology Partners’ purchase of Cognita Imaging, Lunit’s acquisition of Prognosia, and GE’s own purchase of icometrix, completed earlier this month. RadNet also acquired iCAD earlier in the year.

The Takeaway

GE’s acquisition of Intelerad offers multiple benefits to the multimodality OEM, from Intelerad’s presence in the outpatient imaging sector to its experience in cloud-based image management and broad product portfolio. The question is whether the purchase spurs other big iron vendors to answer with acquisitions of their own. 

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. 

Radiology Untethered: Sirona’s Approach to Unified Radiology

Posted on by Partner Content

Radiology stands at a breaking point. 

Hospitals and imaging practices are overwhelmed by fragmented IT systems, cumbersome technology integrations, and staff burnout. Medical imaging is the central hub through which more than 80% of healthcare data flows, but it’s become hobbled by technology that was never designed to work together.

Sirona Medical is changing the equation by rebuilding radiology software from the ground up with a cloud-based architecture that’s as simple as launching a web browser. The company hopes to free radiologists from the constraints of legacy infrastructure and redefine how diagnostic medicine operates in the cloud era, and is demonstrating its approach to radiology professionals.

The fragmented roots of radiology IT. For over two decades, diagnostic imaging has relied on three separate technological worlds that each evolved independently: PACS, reporting, and worklists…

  • PACS revolutionized image storage and viewing in the 1990s, replacing film with pixels. 
  • Reporting software brought speed through voice recognition, ending the days of transcription backlogs. 
  • Worklists organized the chaos of multi-site reading, giving radiologists a unified queue.

Yet these systems were never designed to function as one. Every integration became a brittle patchwork of custom connections. Every update risked breaking the workflow. 

The result was a “house of cards” of on-prem servers, co-located databases, and expensive maintenance contracts. Radiologists found themselves acting as system operators rather than clinical specialists, forced to navigate between screens and dictate into isolated software, losing valuable time that could be spent on patient care.

The hidden cost of separation. This disconnected infrastructure carries enormous financial, operational, and human costs. Hospitals often juggle dozens of software solutions that must be maintained, updated, and bridged by manual effort. 

A single broken link can break the entire workflow. Meanwhile, legacy vendors profit from the complexity, locking customers into long-term contracts that drain budgets and stifle innovation.

The radiologist shortage and rising imaging demand only worsen the problem. Real progress requires not another integration, but a complete re-architecture of the radiology technology stack.

Sirona’s break from the past. Enter Sirona Medical with the mission of rebuilding radiology software as a single, cloud-native platform where PACS, reporting, and worklist live together seamlessly. Delivered entirely through a Chrome browser, Sirona’s system eliminates handoffs, brittle integrations, and costly local servers.

At the platform’s foundation is RadOS, a unified data model and operating system that ingests, normalizes, and orchestrates imaging and text data across formats including DICOM, HL7, FHIR, PDFs, and clinical notes. By consolidating all this information into one consistent data model, RadOS replaces thousands of fragile interfaces with a single source of truth.

RadOS does more than unify; it enables intelligence. Built-in large language and ontology-classification models transform raw imaging and text data into structured, machine-readable insights. As a result, radiologists can work as fast as they can think, and organizations can operate profitably while improving care quality.

Powered by AWS: Streaming radiology to the world. Sirona’s platform runs on AWS, the world’s most robust cloud infrastructure. Sirona delivers massive imaging datasets to radiologists, ensuring near-instant access regardless of geography.

This design provides…

  • Low-latency performance through local caching.
  • HIPAA-compliant, military-grade security across devices and networks.
  • Global reliability backed by AWS’s resilient backbone.
  • Automatic updates via simple browser refresh.
  • Scalable storage without hardware investment.

Hospitals and imaging practices can now connect radiologists worldwide without maintaining physical servers or dealing with VPN bottlenecks.

The application layer: Intelligence built in. Sirona’s application layer sits on top of RadOS and is a seamlessly integrated environment that merges the universal worklist, diagnostic viewer, and AI-driven reporting solution. 

Key capabilities include…

  • Auto-Impressions: AI generates customizable draft impressions, fine-tuned for each reader.
  • Focus Mode: Radiologists dictate naturally while AI maps findings to structured report sections.
  • Quality Assist: A radiology-specific large language model detects speech-to-text errors and clinical inconsistencies in real time.
  • AI Orchestration: Third-party AI tools plug directly into reporting, no brittle middleware required.
  • Priors Summary and Auto-Priors: AI retrieves and summarizes prior exams automatically, accelerating interpretation and ensuring continuity of care.

These features turn the radiology report from a static document into a dynamic, intelligent artifact that supports decision-making across the care continuum.

The time is now for cloud-native PACS, and for the unified approach to radiology viewing, reporting, and worklist that Sirona Medical has pioneered. Radiology’s next era has arrived: one PACS, one worklist, one reporter – and it’s a reality right now.

Learn more about Sirona Medical’s approach to radiology software by booking a demo today.

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.

Target the toughest workflows. Adapt and evolve with Densitas Breast Imaging AI Suite.

AI First Drafts: A New Dawn for Radiology Reporting

Posted on by Partner Content

For radiologists – the medical detectives who find clues in our medical images – the daily grind can feel like a “death by a thousand cuts.” Much of their time is spent not on diagnosis, but on tedious reporting. 

Now, a new generation of artificial intelligence is stepping in to serve as a high-tech scribe, automating the drudgery.

  • This AI tackles reporting, the most time-consuming part of radiologists’ workflow.

AI-enabled radiology reporting makes transcribing data from technologist worksheets a thing of the past, using Optical Character Recognition (OCR) to decipher everything, even what looks like “chicken scratch handwriting.” Then…

  • A large language model (LLM) applies clinical context to ensure it understands the meaning.
  • It intelligently injects that data into the correct sections of the radiologist’s personal report template.
  • Finally, it performs its own “inference,” like calculating a TI-RADS score and dropping it right into the impression.

Modern AI also learns from a radiologist’s actions, providing a hands-free way to build a report, with features such as…

Smart Measurements: When a lesion is measured, the AI recognizes the location and automatically adds the data and comparisons to prior scans into the report.

Automated Prior Population: Instead of struggling with speech-to-text, the AI notices when a prior study is opened for comparison and automatically populates that exam’s date.

Streamlined Expert Findings: A radiologist can simply state positive findings, and the AI acts as both writer and editor. 

AI-enabled radiology reporting weaves dictated phrases into complete sentences, generates an impression based on clinical guidelines like BI-RADS, and serves as a vigilant proofreader, flagging errors like laterality mistakes or semantic impossibilities. 

As AI technology matures, the software itself is becoming easier to build. The true differentiator is the team behind it. 

  • For radiologists evaluating these new reporting tools, it’s critical to look for teams that are “AI native” – built from the ground up with AI at their core. 

Companies founded on these principles, such as New Lantern, are pioneering these all-in-one radiology reporting solutions, treating the challenge not as a problem to be fixed with another widget, but as an opportunity to build one complete, intelligent platform. 

The Takeaway 

The evolution in AI-enabled radiology reporting isn’t about replacing radiologists; it’s a tool to augment their skills. Radiologists who harness AI to create reports faster will significantly outpace those who do not, allowing them to return their full focus to the art of diagnosis.

RadGPT Simplifies Radiology Reports for Patients

Posted on by Brian Casey

When it comes to informing patients of their imaging results, radiologists are caught between a rock and hard place. A new study in JACR shows how generative AI can help by drafting patient-friendly reports that are simple but accurate.

Patients must be informed immediately of their medical results according to a 2021 final rule under the 21st Century Cures Act that prevents medical information blocking. 

  • And while the technology exists to do that through tools like email and electronic patient portals, rapid notification can create confusion because the language physicians use to communicate with each other isn’t easily understood by anyone outside medicine.

Sure, radiology reports could be rewritten manually for patients, who typically read at about the eighth-grade level.

  • But given today’s workforce shortages, who’s going to do that?

Generative AI and large language models offer a solution. In the new JACR paper, researchers from Stanford University led by senior author Curtis Langlotz, MD, PhD, described their development of RadGPT, an LLM designed to improve patient communication.

  • To develop RadGPT, researchers started with OpenAI’s GPT-4 model and the RadGraph concept extraction tool to create an LLM that analyzes patient radiology reports and generates concept explanations and question-and-answer pairs.

How well did RadGPT work? The researchers tested it on 30 radiology reports generated at Stanford from 2012 to 2020, including different modalities and clinical applications. 

  • The LLM was asked to generate reports at a fifth-grade reading level (the level recommended by the Joint Commission for patient-facing healthcare materials).

Five radiology-trained physicians then rated the quality of RadGPT’s responses, finding …

  • The average rating of RadGPT-generated concept explanations was 4.8 out of 5.
  • 95% of concept explanations had an average rating of 4 or higher.
  • 50% of concept explanations were rated 5, the highest possible rating.
  • Questions and answers generated by RadGPT were also rated highly, with an average rating of 3.0 on a three-point scale..

The Stanford researchers told The Imaging Wire that their goal is to make RadGPT more widely available as part of a prospective evaluation with real patients.

  • They are also developing a user-friendly interface in which patients can receive hyperlinked radiology reports.

The Takeaway

RadGPT and solutions like it fill a desperate need for tools that can save time for radiologists while helping patients better understand their reports and get more engaged in their care. The next step is to get technology like this into the hands of practicing radiologists.

Reporting Rules at SIIM 2025

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine offered a great opportunity to take stock of the imaging IT segment. At SIIM 2025, radiology reporting solutions – many powered by AI – were among the most exciting technologies under discussion at Portland’s Oregon Convention Center. 

As we mentioned in our video highlights roundup, attendance seemed a bit lighter at SIIM 2025, perhaps due to the Portland location and timing before a holiday weekend. 

  • But the number of vendors exhibiting at SIIM 2025 cracked 100 for the first time in years, underscoring the meeting’s importance as well as the overall growth of the imaging IT segment as the rise of AI spurs startup creation.

Every SIIM conference provides a fascinating early look at the trends and technologies that will shape radiology’s future, and this year’s meeting was no exception … 

  • Radiology Reporting Rules. The report is the radiologist’s final product, and SIIM 2025 presentations highlighted how important it is to improve this process, especially with AI. An entire track on May 21 was devoted to AI-enhanced reporting solutions, and on the exhibit floor companies showed AI-enhanced solutions that interpret radiologist findings and create structured reports from them. 
  • Questions about AI Adoption. As with past SIIM conferences, questions persist about the pace of AI adoption as well as the FDA’s regulatory direction since the Trump Administration took over. In SIIM 2025’s keynote address, health policy expert Rohini Kosoglu urged SIIM and the radiology community to take a more active role in self-regulation of AI in the absence of stronger direction from the federal government. 
  • Cloud Adoption Gains Steam. There are no such doubts about cloud-based image management, as providers are getting over past concerns about the technology. One enterprise image management vendor told The Imaging Wire that 100% of their new system orders included some form of cloud component. On the other hand, imaging IT expert Herman Oosterwijk sees some imaging sites having “second thoughts” about cloud hosting. 

The Takeaway

The growing prominence of radiology reporting software at SIIM 2025 illustrates the heightened interest in imaging IT solutions that enhance radiologist productivity rather than assist them with interpreting images – a job many feel they can do well enough on their own. 

SIIM 2025 Video Highlights

Posted on by Brian Casey

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.

Keeping Pace with Volume: 7 Strategies from ASNR 2025

Posted on by T. Campbell Arnold

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:

  1. 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.
  2. 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.”
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.

6 Imaging IT Tools Radiologists Want Now

Posted on by Brian Casey

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 …

  1. Increased workstation efficiency, with better tools for looking through medical records to find clinical information. 
  2. Better AI tools for radiology reporting, such as auto-generated measurements and findings from prior studies for comparison. 
  3. Better methods for controlling imaging overutilization, such as clinical decision support systems to be used by referring physicians to order exams.
  4. 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.
  5. Patient engagement tools that promote direct communication between radiologists and patients, including industry-sponsored training modules for radiologists to discuss findings with patients. 
  6. 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. 

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