Aliri AIAliri AI

Use Cases

Intelligence That
Drives Action

From critical findings detection to conversational analytics with Aliri+ — see how structured radiology data powers real clinical and operational outcomes.

Critical Findings Detection

Never miss an urgent finding

Aliri NLP automatically identifies critical and urgent findings — pulmonary embolism, aortic dissection, tension pneumothorax, intracranial hemorrhage — and flags them for immediate clinical attention.

  • Automatic severity classification
  • Real-time alerting
  • Time-to-notification audit trail
  • Configurable rules by modality

Report excerpt · CT chest

...findings consistent with acute pulmonary embolism in the right lower lobe artery. Saddle component present...

URGENT— flagged · radiologist notified 14s ago
Powered by Aliri+

Conversational Analytics

Ask your data in plain English

Department leads, radiologists, and ops teams talk directly to the structured clinical and operational data — describe what you're after and get cited answers in seconds. No analyst queue. No dashboard build. No SQL.

  • Plain-English Q&A across clinical + operational data
  • Cited answers grounded in RadLex, SNOMED, and BI rows
  • Generated tables and charts ready to share
  • Conversation memory · Commercial or on-prem LLM

Has TAT for critical-finding studies changed this quarter?

Median 38 min — up from 31 min in Q4.

critical=trueTAT · BIn=2,184

Incidental Findings Tracking

Close the loop on every incidental

Thyroid nodule · patient timeline

4mmMar 2024
5mmSep 2024
7mmMar 2025
Growth detected — follow-up suggested+3mm

Aliri identifies incidental mentions and links them to prior and future studies for longitudinal tracking.

  • Automatic incidental identification
  • Longitudinal patient timelines
  • Integration with follow-up management

Follow-up Management

Recommendations tracked to completion

Open follow-ups

CT chest — 6mo noduleoverdue
MRI brain — repeat 3modue soon
US abdomen — annualscheduled

Aliri extracts radiologist recommendations, tracks them against the patient record, and flags overdue follow-ups.

  • Recommendation extraction
  • Overdue detection & alerting
  • Completion rate reporting

Quality Assurance

Data-driven quality improvement

Report completeness · last 30 days

Dr. Patel
96%
Dr. Nguyen
89%
Dr. Okafor
78%

Use structured report data for QA: completeness, consistency, guideline adherence, peer comparison.

  • Completeness scoring
  • Terminology consistency
  • Peer comparison dashboards

Cohort Building

Research-grade patient cohorts

Cohort matched

live query

1,247

patients across 8.2M reports

RadLex: lung nodule5-10mmno follow-up

Build patient cohorts by searching structured report data with ontology concepts — find every patient with a finding, anatomy, or combination, regardless of phrasing.

  • Ontology-based semantic search
  • Multi-criteria queries
  • Export to research databases

Operational Analytics

Measure what matters

Incidental rate trend

+1.6pp
9.4%vs. 7.8% (12mo prior)

Leverage structured report data for operational insights — finding distribution, modality utilization, reporting patterns, trend analysis.

  • Finding distribution analytics
  • Trend analysis across time
  • Modality breakdowns
Across every use case

And with Aliri+,
just ask.

Every use case above becomes a conversation. Aliri+ lets you interact with your data in plain English — describe the cohort, the trend, the operational question you're after, and get cited answers in seconds. No SQL, no dashboard-hunting, no waiting on an analyst.

Discover Aliri+

Sample asks

  • “Show me overdue follow-up CTs for pulmonary nodules >8mm.”
  • “Which radiologists most often flag critical findings on night shifts?”
  • “Has the rate of incidental thyroid nodules changed over 12 months?”

Which use case fits your department?

Let's discuss how Aliri AI can address your specific radiology workflow challenges.

Talk to UsExplore the Platform