Why Radiology AI Pre-Reading Is Happening Now, Not Five Years Ago
The model quality crossed a threshold. The backlog crossed a different one. This is why the timing finally makes sense.
Blog
Radiology AI, Workflow, and Clinical Informatics
Writing from the Radivault team on radiology workflow, PACS integration, report drafting, scan backlog, and the practical questions around putting a pre-reading layer inside a real department. We cover what the technology can and cannot do today.
What HIPAA-Aligned Data Controls Actually Mean for a Radiology AI Tool
De-identification, audit logs, BAAs. What each one means in practice for a tool that touches PHI.
HL7 v2 vs FHIR R4: A Plain-Language Integration Primer for Radiology Departments
The two standards your vendor will mention. What they actually do, where each one fits, and what to ask your PACS vendor.
Report Turnaround Time: Four Things That Slow It That Radiologists Cannot Fix Alone
TAT improvement programs focus on the radiologist. Most of the delay is upstream of the reading room.
Radiology Burnout Is a Volume Problem, Not a Complexity Problem
Surveys keep asking about difficult cases. The reading room asks about the number of routine studies waiting.
Clinical Decision Support vs Diagnostic AI: Why the Distinction Matters for Your Department
FDA SaMD classification, radiologist liability, and what labeling AI outputs as CDS actually changes for your department.
Manual Worklist Prioritization vs AI Triage: What Changes on Day One
A side-by-side look at how radiologists currently sort the worklist, and what shifts when a triage layer assigns priority automatically.
What AI Can Draft in a Radiology Report Today (and What It Cannot)
Structured findings language for normal studies: yes. Complex multi-system impressions: not yet. Being honest about the line matters.
Five Throughput Metrics Every Imaging Center Operations Lead Should Track
Study volume, pre-read coverage, draft acceptance rate, TAT by modality, and urgent flag rate. Why each one tells a different story.
Integrating AI Drafts into PowerScribe and MModal: What the Radiologist Actually Sees
A walkthrough of the technical and UX path from AI-generated draft to signed report in the two most common dictation platforms.
Running a Radiology AI Pilot: What to Measure in the First 30 Days
Starting with one modality, tracking the right metrics, and what constitutes a meaningful baseline before expanding.
Is Your Radiology Department Ready for AI? A Practical Checklist
PACS version, HL7 connectivity, workflow documentation, radiologist buy-in. What to assess before you run a pilot.
DICOM SR and Structured Reports: What They Are and Why They Matter for AI Integration
Structured reporting formats are how AI pre-reads surface findings back into your workflow. A plain-language explainer.
Imaging Center vs Hospital Radiology Department: Where AI Workflow Tools Fit Differently
Volume patterns, IT infrastructure, and decision-making speed differ significantly. How to think about AI adoption in each setting.
Why Worklist Design Is a Cognitive Load Problem (Not Just a UI Problem)
The decision a radiologist makes in the first 10 seconds of a shift shapes the whole day. What worklist design gets wrong.
The Scan Backlog Root Cause Analysis: Why It Keeps Growing and What Can Actually Address It
More orders, same reading capacity, inadequate prioritization. Each piece of the backlog equation and where a pre-reading layer fits.