Aliri AI — Radiology Report Intelligence with NLP, Entity Extraction, and Ontology Mapping
Radiology Report
Intelligence
Two layers, one platform. Aliri NLP turns free-text reports into structured, ontology-coded clinical data. Aliri Discuss lets you talk to it — ask questions in plain English across clinical findings and operational BI, and get cited answers in seconds.
- 25M+
- Reports processed
- 500M+
- Entities extracted
- <0.2s
- Processing time per report
- 99.2%
- Entity accuracy
How It Works
From Free Text to Conversation
One end-to-end flow. Aliri NLP structures every report; Aliri Discuss lets you talk to the result.
Ingest
Radiology reports ingested from RIS, PACS, or HL7 feeds in real time.
Aliri NLPParse & Extract
NLP engine identifies anatomy, findings, modifiers, and measurements from free text.
Aliri NLPMap Ontologies
Each entity is mapped to RadLex and SNOMED CT codes for standardized interoperability.
Aliri NLPSearch & Act
Query structured data by ontology concepts, build cohorts, and trigger follow-ups.
Aliri NLPAsk & Analyze
Aliri Discuss adds a conversational layer on top — ask plain-English questions across clinical and operational data, get cited answers in seconds.
Aliri DiscussCapabilities
Built for Radiology. Powered by NLP.
Every entity in every report — identified, classified, and mapped to standard ontologies.
Entity Extraction
Identifies anatomy, pathology, measurements, laterality, and modifiers from every report with high precision.
RadLex Mapping
Every extracted entity is automatically assigned a RadLex identifier — the standard ontology for radiology terminology.
SNOMED CT Coding
Entities are cross-mapped to SNOMED CT concepts, enabling interoperability across clinical systems and research databases.
Critical Findings
Automatically detect urgent and critical findings requiring immediate clinical attention or follow-up action.
Cohort Searching
Search across millions of structured reports using ontology concepts — find every "hepatic lesion" regardless of how it was described.
Follow-up Management
Track recommended follow-ups, detect overdue studies, and close the loop on incidental and critical findings.
Standards-Based
Multiple Ontologies,
One Platform
Aliri maps every extracted entity to both RadLex — the comprehensive radiology lexicon maintained by RSNA — and SNOMED CT, the world's most comprehensive clinical terminology. This dual-mapping enables both radiology-specific analytics and cross-system interoperability.
Entity: "hepatic steatosis"
RID4566
197321007
Entity: "right renal cyst"
RID34572
126484007
Entity: "pulmonary embolism"
RID4868
59282003
Now: talk to your data
in plain English.
Aliri Discuss sits on top of Aliri NLP and your BI feeds. Ask questions in plain English across clinical findings and operational metrics (RVU, TAT, shifts) — no SQL, no dashboards, no waiting. Get cited, grounded answers in seconds. Commercial API or local on-prem LLM for PHI safety.
“How has the rate of incidental thyroid nodules on CT chest changed in the last year?”
Up to 9.4% from 7.8% in the prior year.
Ready to unlock the intelligence in your reports?
See how Aliri AI can transform your radiology department's unstructured data into actionable clinical intelligence.