Psychiatric data labelling by expert psychiatrists.
We provide psychiatric and mental health data labelling and AI evaluation, combining psychiatrists from our clinical network with PhD data scientists.
Mental health data is nuanced, sensitive and often ambiguous. The people labelling it should understand it.
Mental health data, reviewed with care.
Psychiatric data needs psychiatric judgement.
Ambiguity is built in
Symptoms overlap, and the same words can mean different things in different contexts. Agreement between reviewers is often lower than in other specialties, so independent review and adjudication matter more.
Risk needs a plan
Mental health data can include self-harm and suicide. We agree with you how risk is defined, graded and flagged before any review starts.
The data is sensitive
Data access, handling and confidentiality are agreed during scoping. Reviewers sign confidentiality and data protection agreements before they see any project data.
Clinical judgement, measured properly.
We agree the labelling criteria, risk definitions and review design with you during scoping. Where appropriate, each item is reviewed independently by more than one psychiatrist, and disagreements are resolved by a senior reviewer.
PhD data scientists measure how often reviewers agree, so you know how reliable the labels are and where the task is genuinely hard. Read our guide to ground truth and reviewer agreement.
WHAT YOU RECEIVE · DEPENDING ON SCOPE
- Labelled datasets
- Clinically reviewed labels
- Risk and severity grading
- Expert-reviewed AI responses
- Reviewer agreement analysis where appropriate
- Documented labelling criteria
- Structured report with recommendations
Our evaluations provide clinical insight to support product development. They do not certify clinical safety, confer regulatory approval or replace formal validation requirements.
Often combined with.
Let's solve your next clinical AI challenge.
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