aceAn artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Phenotyping is fundamental to rare disease diagnosis, but manually curating structured phenotypes from clinical notes is labor intensive and difficult to scale. Existing artificial intelligence (AI) approaches optimize individual components of phenotyping but generally do not operationalize the full workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing rare disease-relevant HPO terms. We developed RARE-PHENIX, an end-to-end AI framework f…
RARE-PHENIX automates extraction, HPO standardization and ranking of phenotypes from clinical notes, producing terms highly concordant with clinician curation and reducing reliance on labor-intensive manual curation.
The authors frame the findings cautiously, describing only potential downstream disease-prioritization signal and possible support for human-in-the-loop use rather than demonstrated autonomous diagnostic benefit.
Evidence
- Peer-reviewednpj Digital Medicine2026-10-15
How should this claim be treated?
Truvace Impact Record TRV-2026-1594, v1: “An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models.” Truvace, 2026-10-16. /record/TRV-2026-1594 (accessed at citation time). sha256 cd37f86d39b7ef63…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1594 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.