Scaling Biomedical Knowledge with AI + Human Curation

The challenge

Published scientific literature is growing exponentially, but extracting, structuring and validating biomedical insight from it remained largely manual. Clinical teams and researchers needed timely, accurate, standardized data, and traditional curation could not keep pace.

• Thousands of new publications every month across oncology, genomics and rare disease.
• Inconsistent data formats across journals and databases.
• High cost and slow turnaround of manual expert curation.
• Clinical decisions delayed by incomplete or outdated evidence.

The solution

OptraHEALTH built and operated end-to-end curation workflows that combine natural language processing, schema normalization and expert human review: ingest, extract, normalize, validate.

Semi-automated extraction

AI-assisted workflows parse peer-reviewed literature and extract gene-disease associations, variant classifications and therapeutic evidence.

Schema normalization

Extracted data is mapped to standard ontologies, including HGVS, ClinVar and HPO, so it works across systems.

Expert validation

A dedicated team of trained bio-curators reviews AI output, resolves edge cases and ensures clinical-grade accuracy.

Results

Curation cycle times fell by more than 70% while accuracy stayed above 98%, letting institutions act on new evidence weeks ahead of traditional workflows.

A leading academic medical center

Accelerated oncology evidence synthesis for tumor boards. Curated variant interpretations cut molecular tumor board preparation time by 60%.

A nationally recognized research hospital

Large-scale systematic review support for rare disease genomics, with structured datasets powering meta-analyses. Literature review phases shrank from months to weeks.

A genomics and precision medicine company

A curated genomic knowledge base supporting clinical interpretation pipelines behind patient-facing genetic reports, delivered at scale.