Platform · Aliri NLP
The NLP Engine Behind
Radiology Intelligence
Aliri NLP is the entity-extraction and ontology-mapping pipeline at the heart of Aliri AI. It transforms free-text radiology reports into structured, ontology-coded clinical data — every entity, every report, automatically.
Looking for the AI analyst layer? See Aliri Discuss.
Pipeline
Four Stages, One Seamless Flow
From raw text to structured intelligence — here's how every report is processed.
Stage 01
Report Ingestion
Connect to any RIS, PACS, or HL7 feed. Aliri normalizes report structure — handling headers, sections, impressions, and addenda — before NLP processing begins.
- HL7 v2 / FHIR support
- Real-time streaming or batch import
- Section segmentation (Findings, Impression, History)
- Multi-site, multi-modality support
Stage 02
NLP Entity Extraction
A purpose-built medical NLP pipeline identifies clinical entities from free-text radiology reports. The engine handles negation, uncertainty, laterality, and temporal context.
- Named entity recognition (NER)
- Negation and uncertainty detection
- Relationship extraction (finding → anatomy)
- Context-aware entity linking
Stage 03
Ontology Mapping
Each extracted entity is mapped to RadLex and SNOMED CT concepts using a combination of dictionary lookup, embedding similarity, and rule-based disambiguation.
- RadLex (RSNA standard)
- SNOMED CT clinical terminology
- ICD-10 cross-reference (optional)
- Confidence scoring per mapping
Stage 04
Structured Output
The result: a fully structured, queryable representation of every report. Each entity carries ontology codes, section context, negation status, and relationship links.
- JSON / FHIR-compatible output
- Searchable entity database
- Cohort builder integration
- Analytics-ready structured data
Entity Recognition
What Aliri Extracts
The NLP engine recognizes and classifies nine core entity types from every radiology report.
right upper lobe, liver, left kidney
nodule, mass, effusion, fracture
pneumonia, cirrhosis, lymphoma
5mm, 2.3 x 1.8 cm
stable, increased, new, unchanged
right, left, bilateral
positive, negative, no evidence of
definite, probable, possible, suspected
follow-up CT in 6 months, MRI recommended
Ontology Mapping
RadLex + SNOMED CT, Automatically
Every entity is assigned standardized codes from both RadLex and SNOMED CT, enabling cross-system interoperability and research-grade data quality.
High-confidence mapping
Confidence scores on every mapping. Low-confidence results are flagged for review.
Continuous vocabulary updates
Ontology dictionaries are updated with each RadLex and SNOMED release.
Cross-mapping support
Navigate between RadLex and SNOMED via built-in cross-reference tables.
{
"report_id": "RPT-20260329-4182",
"entities": [
{
"text": "hepatic steatosis",
"type": "FINDING",
"negated": false,
"radlex": {
"id": "RID4566",
"term": "hepatic steatosis",
"confidence": 0.97
},
"snomed": {
"id": "197321007",
"term": "Steatosis of liver",
"confidence": 0.95
}
},
{
"text": "right kidney",
"type": "ANATOMY",
"radlex": {
"id": "RID29662",
"term": "right kidney"
},
"snomed": {
"id": "9846003",
"term": "Right kidney structure"
}
}
]
}Cohort Searching
Search by Concept, Not by Keyword
Because radiologists describe the same finding in dozens of ways. Aliri searches by ontology concept — so you never miss a match.
Synonym Resolution
Search for "hepatic steatosis" and also find "fatty liver", "steatohepatitis", and "fatty infiltration of liver".
Hierarchical Navigation
Search for "lung nodule" and optionally include all child concepts — ground-glass nodule, calcified nodule, subsolid nodule.
Complex Queries
Combine anatomy + finding + temporal filters: "all pulmonary embolism findings in the last 90 days, excluding negated mentions."
+ Pair with Aliri Discuss
Aliri NLP structures the data.
Aliri Discuss lets you talk to it.
Once Aliri NLP has structured your reports, Aliri Discuss lets you talk to the result. Ask questions in plain English across clinical findings and operational BI data — no SQL, no dashboards, no waiting — and get cited, grounded answers in seconds.
Explore Aliri Discuss“Show me CTs with a 5–10mm pulmonary nodule in the last 6 months — and the radiologists whose follow-up rate is below average.”
See the platform in action
Schedule a technical demo and see how Aliri processes your radiology reports.
Request a Demo