Tag: radiology reporting

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 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. 

Intelerad’s Reporting Play

Posted on by Jake Fishman

Intelerad continued its M&A streak, acquiring radiology reporting company, PenRad Technologies, in a relatively small deal that might have a much bigger impact than some think.

PenRad has a solid share of the breast and lung cancer screening reporting segments, making it a target of a number of PACS vendors in recent years.

The acquisition is another example of Intelerad using its private equity backing to complete its informatics portfolio, following a series of deals that allowed its expansions into new clinical areas (cardiac, OB/GYN), regions (UK), technologies (cloud), and functionalities (image sharing, cloud VNA).

Adding PenRad will immediately give Intelerad three proven cancer screening reporting solutions to offer to its PACS customers, while bringing Intelerad into an untold number of PenRad accounts that it didn’t work with before now. 

The deal’s long-term impact will likely be dictated by how well Intelerad integrates and enhances its new PenRad technologies. If Intelerad is able to seamlessly integrate its PACS/worklist with PenRad’s dictation/reporting, it could create a truly unique advantage — especially if Intelerad expands its reporting capabilities beyond just cancer screening. 

Intelerad’s PenRad acquisition and Sirona’s unified radiology platform also highlight the differentiating role that integrated reporting might play in future enterprise imaging portfolios, although there aren’t many more reporting companies still available for acquisition.

The Takeaway

Informatics veterans might point out that it’s much easier to acquire a portfolio of companies than it is to integrate all that software — and they’d be correct. That said, most would also agree that Intelerad has assembled a uniquely comprehensive enterprise imaging portfolio and it would be extremely well-positioned if/when that portfolio becomes fully integrated.

Radiology’s Smart New Deal

Posted on by Jake Fishman

A new Journal of Digital Imaging editorial from UCLA radiology chair Dieter R. Enzmann, MD proposed a complete overhaul of how radiology reports are designed and distributed, in a way that should make sense to radiology outsiders but might make some folks within radiology uncomfortable.

Dr. Enzmann’s “Smart New Deal” proposes that radiology reports and reporting workflows should evolve to primarily support smartphone-based usage for both patients and physicians, ensuring that reports are:

  • Widely accessible 
  • Easily navigated and understood 
  • Built with empathy for current realities (info overload, time scarcity, mobility)
  • And widely utilized… because they are accessible, simple, and understandable

To achieve those goals, Dr. Enzmann proposes a “creative destruction” of our current reporting infrastructure, helped by ongoing improvements in foundational technologies (e.g. cloud, interoperability) and investments from radiology’s tech leaders (or from their future disruptors).

Despite Dr. Enzmann’s impressive credentials, the people of radiology might have a hard time coming to terms with this vision, given that:

  • Radiology reports are mainly intended for referring physicians, and referrers don’t seem to be demanding simplified phone-native reports (yet)
  • This is a big change given how reports are currently formatted and accessed
  • Patient-friendly features that require new labor often face resistance
  • It might make more sense for this smartphone-centric approach to cover patients’ entire healthcare journeys (not just radiology reports)

The Takeaway

It can be hard to envision a future when radiology reports are primarily built for smartphone consumption.

That said, few radiologists or rad vendors would argue against other data-based industries making sure their products (including their newsletters) are accessible, understandable, and actionable. Many might also recognize that some of the hottest imaging segments are already smartphone-native (e.g. AI care coordination solutions, PocketHealth’s imaging sharing, handheld POCUS), while some of the biggest trends in radiology focus on making reports easier for patients and referrers to consume.

Smartphone-first reporting might not be a sure thing, but the trends we’re seeing do suggest that efforts to achieve Dr. Enzmann’s core reporting goals will be rewarded no matter where technology takes us.

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