From task definition to acceptance

LumeSage · MEDICAL & LIFE SCIENCES AI DATA

From task definition to acceptance

Verifiable specialist data, expert production and ongoing evaluation for medical and life sciences AI teams.

Turn data gaps into clearly scoped, reviewable deliverables. Verify requirements, samples and permissions, then move from a pilot to production and version evaluation.

Three services across the development cycle

Procure each service separately or connect them in stages, with agreed inputs, deliverables and acceptance criteria.

01 / Specialist data

Data matched to your task

Verify sources, strata, counting rules and permissions for imaging, clinical text and multimodal tasks before agreeing a data card and pilot.

Data card · File manifest · Quality and permission notes

Specialist data

02 / Expert production

Turn expertise into training material

Scope annotation, review, preference judgments or evaluation design. Specialist fit, qualifications, effort and review methods are agreed per project.

Task rubric · Review records · Versioned outputs

Expert production

03 / Ongoing evaluation

Understand changes and failure types

Medical Gate uses agreed private tasks and scoring rules. Define retest scope, cadence and independence arrangements in the project agreement.

Scoring rubric · Failure analysis · Version comparison

Ongoing evaluation

Start with a pilot; expand in four steps

  1. 01 · Scope

    Agree the use case, data conditions, outputs and acceptance method.

  2. 02 · Verify and pilot

    Check permissions and expert fit; agree a paid pilot and exit criteria.

  3. 03 · Produce and accept

    Work to frozen specifications, with quality, rework and file verification records.

  4. 04 · Retest and iterate

    Retest as agreed. Translate failure categories into new task specifications.

What makes a deliverable verifiable

Agree the content, access level and timing of these materials before delivery.

Task and counting
Define modality, inclusion rules, deduplication keys and units. Cases, studies, series, slices and annotations are distinct.
Source and permissions
Check provenance, lawful processing basis, training use, retention and redistribution. De-identification does not replace authorization.
Samples and distributions
Review samples, fields, strata and missingness. Sample approval is not batch acceptance.
Expertise and quality
Specify expert fit, review, sample denominators, disagreement resolution, rework and acceptance records.
Files and versions
Agree paths, checksums, versions, format checks and image–text pairing. Settle against accepted, deduplicated units.
Evaluation separation
Freeze tasks and rubrics; agree access, contamination checks, retest conditions and role disclosure.

How evaluation informs the next production cycle

Use failure categories and new task specifications as feedback. Do not automatically return private holdout questions or answers to training. Where one provider performs production and evaluation, disclose roles and agree personnel, access and test-set separation, including independence limitations. Evaluation supports development decisions; it does not replace clinical validation or regulatory approval.

Bring a task; start with verifiable outputs

Share the objective, modality, data readiness, acceptance needs and timing. Do not submit patient information or raw medical data through public forms.

Discuss a task and pilotDelivery & acceptance