Biomedical Subject Matter Expert

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The National Library of Medicine (NLM) is launching Biomedical AI Challenges — a structured prize competition program designed to catalyze innovation in AI-powered tools for biomedical research infrastructure. The program seeks to improve AI-enhanced semantic search across Pub Med/PMC and establish foundational data interoperability by mapping raw, cohort-specific dbGaP study variables directly to precise, standardized international vocabularies/ontologies (such as LOINC, RxNorm, SNOMED CT, and the UMLS Metathesaurus).

Right now, dbGaP study variables and metadata are described in disparate, cohort-specific shorthand rather than standardized codes. Because standardized terminologies attach stable, unambiguous codes to clinical concepts, they are what make it possible for different studies, systems, and AI tools to "talk" about the same phenotype, lab result, or exposure in the same way. Without that shared vocabulary layer, cross-study comparison stays manual and error-prone, dbGaP's rich phenotypic data remains difficult to discover, and researchers can spend months pursuing a controlled-access request only to find the cohort doesn't match their needs.

The SME's terminology mapping and curation work is the foundation this entire Challenge is built on: it is what turns free-text variable descriptions into the standardized, computable concepts that both Challenge tracks — and, ultimately, the broader research community — depend on for reliable semantic search and cross-study interoperability.

The Biomedical Subject Matter Expert (SME) serves as a senior scientific advisor and technical authority to provide expert guidance on evaluation framework development, benchmark dataset validation, and scientific review. The Biomedical SME contributes meaningfully to shaping the scientific integrity and rigor of challenge-related tasks.

• Map dbGaP variables to controlled vocabularies and common data elements, including PhenX measurement protocols, using the dbGaP data dictionary's  and _ID fields, and classify mappings by confidence level (e.g., identical, comparable, or related) consistent with dbGaP/PhenX conventions.

• Curate and validate semantic annotations linking dbGaP variables to standard terminologies such as the UMLS Metathesaurus, LOINC, and UMLS Concept Unique Identifiers (CUIs), following the approach used by groups like NLM's Lister Hill Center, Medical Data Models, and NHLBI's TOPMed program.

• Support FHIR-based interoperability by ensuring annotated vocabularies are correctly represented in the dbGaP FHIR schema and accessible via the dbGaP FHIR API.

• Design and curate the expert-adjudicated reference ("gold standard") mapping set against which Track 1 participant submissions are scored, including explicit criteria for the  discard class (variables with no valid target-vocabulary concept).

• Author and maintain annotation guidelines and adjudication protocols so reference-set decisions are reproducible and defensible under review or challenge.

• Run or oversee inter-annotator agreement checks across SME reviewers and resolve disagreements before a mapping enters the reference set.

• Advise on Track 2 relevance judgments — labeling dbGaP studies as relevant/not relevant to natural-language research queries — in a form suitable for computing ranking metrics.

• Contribut…