Clinical evaluation and specialist annotation for healthcare AI.
We help healthcare AI teams evaluate model behaviour and build reliable clinical datasets. Specialty-matched clinicians work alongside PhD data scientists and AI engineers, from programme design through delivery.
Programmes can be designed and delivered in your environment where appropriate, or alongside your existing team.
Access to a network of 30,000+ clinicians through BiteLabs.
Clinical modalities
- Medical imaging
- Clinical text, EHR & NLP
- Audio
- Video
- Other clinical media on request
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30,000+ clinicians. One specialist network.
DeepLabel draws on the BiteLabs network of more than 30,000 clinicians to build project teams around specialty, geography, credentials and availability.
Selection, matching, calibration and quality controls are defined for each programme.
Explore the BiteLabs networkClinical expertise, built into the workflow
The difficult cases need more than a generic label.
Whether you are assessing a model’s clinical reasoning, building a reference standard or preparing data for the next training cycle, the work depends on the right specialists, clear criteria and a workflow that captures disagreement rather than concealing it. We design and run that work with your team.
Three complementary offers
Clinical work, built around the question at hand.
Evaluation and annotation receive equal attention, with training data available where it supports the wider programme.
Clinical AI Evaluation
Find out where a model performs well, where it fails and how serious those failures are. We support clinician review of outputs, scoring frameworks, benchmark design, preference data, medical red-teaming and analysis of failure modes.
Explore evaluation
Specialist Clinical Annotation
Build clinical labels and reference datasets for development or validation. We can help define a protocol, recruit the right specialists, operate in your platform or infrastructure and manage quality checks, consensus and provenance.
Explore annotation
Clinical Training Data
Create task-specific clinical examples, including questions and answers, case vignettes and expert feedback, to support model development and iteration. Scope and review are tailored to the intended use.
Explore training data
How we work
From an early question to a working programme.
- 01
Programme design
Clarify the clinical task, data, evaluation criteria, platform and outputs.
- 02
Specialist delivery
Assemble and coordinate clinicians matched to the specialty, geography and required credentials; work in your environment where appropriate.
- 03
Quality and insight
Calibrate reviewers, manage QA and disagreement, and return usable findings for model development or validation.
- 04
Embedded support
Work alongside your team where you need clinical, data science or AI engineering capacity.
Types of work
Work designed for the clinical question at hand.
Clinical review of model outputs and severity-based failure analysis
Evaluation frameworks, scoring rubrics and benchmark protocols
Specialist reference-standard annotation and provenance
Preference datasets and clinical red-teaming
Project brief
Tell us what you need to evaluate or build.
Share the clinical use case, modality, approximate volume and where you are in development. We will suggest a practical approach.