Match the expertise
Confirm the research question, disciplines and expert responsibilities. Verify individual qualifications, relevant experience, availability and confidentiality arrangements during scoping.
LUMESAGE / EXPERT DATA
Expert involvement with reviewable evidence
LumeSage supports biological and medical AI data projects through an expert team. These are the working practices and evidence to agree during scoping; the actual configuration is project-specific.
How experts contribute
Define review responsibilities
Agree who owns the task, annotates, reviews and resolves disagreements. Independent double review or blinded review is selected to fit the task and risk.
Document acceptance
Agree on protocol versions, review records, issue logs and an acceptance report. Specify expert involvement and review coverage in the project scope.
Confirmed foundation: an expert team. Individual disciplines, qualifications and publishable examples are verified per project. Unconfirmed titles or institutional affiliations are not used as endorsements.
Explore expert involvement and verification →What you can verify before engagement
- Relevant disciplines and their fit to the task.
- Qualifications and experience that may be disclosed, plus participation arrangements.
- Proposed annotation protocol, expert review coverage and disagreement process.
- Authorized protocol excerpts or controlled examples, rather than unlicensed raw data.
- Agreed confidentiality, conflicts of interest, delivery responsibilities and acceptance records.
Shared expert governance, distinct task evidence
Biological AI
Define expert work around the research question, experimental conditions, label evidence and data splits.
Medical AI
Define review around clinical context, annotation judgments, case consistency and safety boundaries.
Medical Gate
Private medical-model evaluation uses an agreed test set, scoring rules and failure taxonomy. Biological research evaluation is scoped separately.