Model Need Identification
Turn vague clinical AI ambitions into a concrete, defendable dataset specification: modality, volume, labels, demographics, and quality thresholds.
Overview
Most medical AI programs spend their first six months collecting the wrong data. Engineering describes what they need in implementation terms; clinicians describe it in workflow terms; data vendors quote against neither. The result: expensive, ill-fitting datasets and avoidable model retrains.
We translate the product's intended use, the candidate model architecture, and the regulatory expectations into a single dataset specification document. Once signed off, that document drives every downstream vendor conversation, every legal agreement, and every quality acceptance check.
Our Process
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1
Intended use & model framing
Sessions with product, clinical, and ML leads to ground the dataset in the actual prediction task.
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2
Modality & label decomposition
Specify imaging modality, capture parameters, label schema, label granularity, inter-rater requirements.
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3
Volume & demographics math
Power-analysis driven targets across subgroups required for representative validation.
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4
Quality criteria
DICOM tag completeness, label provenance, source-site diversity, exclusion criteria.
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5
Specification sign-off
Written document signed by product, clinical, and regulatory leads, frozen for vendor sourcing.
Frequently Asked Questions
Does this work for non-imaging AI?
Yes, specifications adapt for waveform, EHR, multi-omic, and longitudinal datasets.
How do you decide subgroup volumes?
Power analysis grounded in the validation precision required for FDA / Notified Body submission, plus GMLP representativeness principles.
Who needs to sign off?
Product, clinical lead, regulatory, and ML/data science. Adding more signers slows iteration without adding rigor.
Can the specification be updated later?
Yes, with a documented change log. Frozen specifications survive the sourcing cycle; they evolve between cycles.
Stop collecting the wrong data.
Share your intended use and current dataset gaps. We will return a draft dataset specification within three weeks.
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