Reporting Redefined
Reports that start from the image, not a blank page.
Legacy reporters process text and audio — they've never seen the images. Sirona's FDA Class I reporter ingests DICOM pixel data directly, so AI can work from the images while you read.
How Sirona is Different
You can't accelerate reporting without pixel data
Reporting platforms have spent two decades optimizing text workflows — better speech recognition, smarter templates, faster macros. But the images themselves have always been invisible to the reporter. That's a ceiling. AI can now auto-label anatomy, surface findings, propose measurements, and draft impressions for a radiologist to review — but only if it can work from the images. Sirona's reporter ingests DICOM pixel data natively, even when deployed as a standalone reporting solution without the viewer. Every AI acceleration that matters in the next decade starts with pixel access.
What pixel access enables
When the reporter can see the images, everything about reporting changes — from anatomy labeling to impression drafting to the long-term trajectory toward fully AI-generated reports.
Anatomic Navigation
Automatic spine, knee, and shoulder labeling directly in the report. Click a vertebral level in the report and the viewer highlights the anatomy. Click anatomy in the image and the report populates. High auto-acceptance in everyday reading.
Explore Spine Suite
Bi-Directional Pixel-Word Link
Every finding in the report is linked to the region of the image it describes. Click the text, see the anatomy. Click the anatomy, populate the report. The reporter and the image are finally connected.
Explore Reporter
AI-Drafted Impressions
Auto-Impressions reads the images, the findings, the priors, and the radiologist's dictation pattern — then drafts an impression in seconds. Personalized to each radiologist.
Explore Auto-Impressions
Real-Time Quality Assist
AI that checks the report against the images as you dictate — flagging discrepancies, missing findings, and measurement inconsistencies before you sign. Only possible when the reporter sees the pixels.
Explore Quality Assist
Automated Measurements
AI-derived measurements from the images flow directly into the report with structured data and linked coordinates. No manual entry, no transcription errors, no copy-paste from the viewer.
Learn more
Foundation Model Trajectory
Today: AI assists with impressions, labels anatomy, and checks quality. Tomorrow: foundation models that draft complete reports from images alone. A reporting platform positioned for this future is one that already ingests pixels.
Platform AIThe pixel-powered argument, in three parts
Speed Ceiling
Text-only reporting has hit its ceiling
Macros, templates, and better speech recognition can only take you so far. The step change in reporting velocity comes from AI that can see the image alongside the radiologist — auto-labeling anatomy, pre-populating measurements, drafting impressions. Every one of those capabilities requires pixel access.
Anatomic labeling can't exist without pixels
Auto-Impressions needs image context to ground its drafts
Real-time QA compares text to what's actually in the image
Every next-generation acceleration starts with pixels
Architecture
Why legacy reporters can't catch up
Traditional reporters are HL7-in, HL7-out systems — text architecture, not image architecture. And analyzing pixels for clinical use means being an FDA Class I medical device, which requires design controls, verification, and validation traced back to the product's inception. That can't be retrofitted onto an existing product — so legacy reporters must rely on someone else for pixel analysis, and lose value as pixels take over the reporting workflow.
HL7-only data model by design
Class I status requires design history back to inception
Reverse-engineering into a device platform isn't feasible
Outsourcing pixel analysis means losing value over time
AI-Native Gateway
Pixel-native is the entry condition for AI-native
Multimodal AI — the thing that will reshape radiology over the next decade — analyzes images, text, and audio in one pass. A reporting platform without pixel access is structurally locked out. Choosing a text-only reporter today locks you out of tomorrow's AI roadmap.
Multimodal foundation models require pixels
Agentic workflows coordinate across image and text
Voice-to-action commands need pixel context
The AI roadmap starts at pixel access
Performance
What pixel-powered reporting delivers today
This isn't a future promise. Sirona's pixel-powered reporter is running in production today — accelerating reporting, drafting impressions radiologists accept, and auto-labeling anatomy. Every radiologist on the platform gets the pixel advantage without any custom integration.
Faster reporting with AI assistance
High Auto-Impressions acceptance
Reliable Anatomic Navigator labeling
FDA Class I device platform, production-grade
FDA
Class I medical device platform — built for pixels from day one
Faster
reporting with pixel-powered AI assistance
Trusted
Auto-Impressions radiologists accept
What pixel-powered reporting feels like
Watch the demo 10-Minute Demo: Why Unified Data is Necessary for Reporting's Future
Sirona's Chief Technology Innovation Officer, Dr. Mark Longo, demonstrates why Sirona's unified platform is necessary for the future of reporting AI. If AI is going to transform radiology reporting, that process starts with pixels and ends with action — as Dr. Longo illustrates with a demo of Sirona's production platform.
“We built our AI on Sirona because it's the only infrastructure where the model, the images, and the report can live in one place.”
Rustin Rassoli
Founder & CEO, Epsilon Health
FAQs
What makes a reporter 'pixel-powered'?
Why can't PowerScribe do this?
Does this work as a standalone reporter?
Is Sirona an FDA medical device?
How does this pair with the Platform AI features?
What's the long-term roadmap?