Description
DeepLabel provides clinical AI evaluation and data services for healthcare AI teams, including clinician-led evaluation, red-teaming, benchmarking, preference data and specialist annotation, drawing on the BiteLabs network of UK (NHS) and US clinicians. Founded in 2026 by Dr Azeem Alam BEM, DeepLabel is based in London and is part of BiteWorld Ltd, a sister company to BiteLabs. It works with healthcare AI companies in the UK, US and beyond, pairing specialist clinicians with PhD data scientists who set the review criteria, measure agreement between clinicians and resolve disagreements.
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Its services cover clinical AI evaluation (clinical accuracy testing, safety evaluation, clinical LLM evaluation and medical red-teaming), medical data annotation and ground truth for images and clinical text, medical imaging annotation, clinical training data and expert feedback including RLHF preference data, AI scribe evaluation against the source consultation, and psychiatry and mental health data labelling. Deliverables include evaluation results and structured reports, clinically reviewed datasets, and reviewer agreement analysis. Many projects combine more than one service in a single engagement.
A project starts with a conversation about your product, its clinical use case and the data involved. DeepLabel then proposes the clinical expertise, review method, scope and deliverables, and agrees them with you before work begins. It can also review your existing labels and training data for clinical accuracy and consistency. Data access and confidentiality are agreed during scoping. Projects are scoped individually, from £10,000 + VAT, and DeepLabel replies the same working day.
- CategoryMedical & Clinical AI
- Pricing ModelCustom
DeepLabel's Core Features
Clinician-led clinical AI evaluation
Medical red-teaming and safety evaluation
Medical data and imaging annotation
RLHF preference data and expert feedback
AI scribe evaluation
Psychiatry and mental health data labelling
BiteLabs network of 30,000+ UK (NHS) and US clinicians
PhD data scientists measure clinician agreement
How to use DeepLabel?
Get in touch: Share your product, clinical use case, data and timeline
Scoping: DeepLabel proposes the expertise, review method, scope and deliverables and agrees them with you
Delivery: Receive structured clinical data, evaluation findings or expert feedback
DeepLabel's Use Cases
- Clinical LLM Evaluation
- Medical Imaging Ground Truth
- AI Scribe Review
- Model Training Data
- Mental Health AI
