Radiologist
Generate and review structured radiology reports
What You Do Today
Dictate findings using structured templates, compare with priors, measure lesions per RECIST or other criteria, assign BI-RADS/LI-RADS/TI-RADS scores, and generate actionable impressions.
AI That Applies
Reporting AI auto-populates measurements, generates comparison language from priors, suggests standardized scoring, and drafts structured reports from voice dictation with auto-formatting.
Technologies
How It Works
The system ingests voice dictation with auto-formatting as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — comparison language from priors — surfaces in the existing workflow where the practitioner can review and act on it. The impression is yours — the synthesis of findings into a diagnosis and recommendation.
What Changes
Reports are faster to generate. AI pre-fills measurements, comparison language, and scoring. Dictation accuracy improves with radiology-specific language models.
What Stays
The impression is yours — the synthesis of findings into a diagnosis and recommendation. AI can format the report. You provide the conclusion that changes patient management.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for generate and review structured radiology reports, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long generate and review structured radiology reports takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your department medical director
“Which of our current reports are manually assembled, and how much time does that take each cycle?”
They set clinical practice guidelines that AI tools must align with
your health informatics lead
“What questions do stakeholders actually ask that our current reporting doesn't answer?”
They manage the EHR integrations and clinical decision support configuration
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.