Use case · builds on Clinical AI Evaluation
Clinical AI evaluation for pharma and life sciences.
Specialists matched to your therapy area review AI that writes, summarises and answers medical questions, from medical information chatbots to literature review and medical writing tools. PhD data scientists design the review and report findings by type and severity. We also build clinically reviewed datasets to improve these models.
- Who it's for
- Pharma, biotech and life sciences teams building or buying AI for medical content.
- The problem
- Medical content generated by AI has to be accurate, balanced and supported by the source.
- Clinical and data science expertise
- Specialists matched to your therapy area, with PhD data scientists who design the review and report the findings.
- How quality is assessed
- Review criteria are agreed with your team before work begins, and agreement between reviewers is measured where appropriate.
NDA and data processing agreement on every project. How we handle your data.
Clinical review for AI that writes medicine.
Medical information and HCP chatbots
Clinician review of answers for accuracy, completeness, balance and safety-relevant errors before and after launch.
Literature review and evidence synthesis AI
Specialists check extracted outcomes, summaries and citations against the source papers for omissions and unsupported statements.
Medical writing and content assistants
Expert review of generated medical content, with findings graded by type and severity.
Clinical datasets for model development
Annotation and review of clinical text, imaging and other medical data, and reference test sets for evaluation.
Expert feedback for domain LLMs
Clinician-authored examples, response ratings and preference data for models in your therapy areas.
- Clinical error findings
- Reviewed datasets
- Structured reports
Our evaluations provide clinical insight to support product development. They do not certify clinical safety, confer regulatory approval or replace formal validation requirements.
Need to hear from healthcare professionals?
HCP market research, including interviews and surveys, is run by our sister company BiteLabs through BiteLabs Pulse. DeepLabel focuses on evaluating AI and building clinical data. If your project needs both, we can scope it together.
Visit BiteLabs PulseScoped with you, from the start.
Every project starts with a scoping call about your product, data and goals, followed by a clear proposal. How it works.
- 01
Specialists matched to your therapy area and use case
- 02
Review criteria agreed with your team before work begins
- 03
PhD data scientists measure agreement between reviewers where appropriate
- 04
An NDA and a data processing agreement on every project. How we handle your data
A medical information answer, reviewed.
Illustrative example
Specialists matched to your therapy area review each answer and grade every issue by severity.
- User
Is Product X safe to take while breastfeeding?
- Model
Yes. Product X is completely safe while breastfeeding.
Unsupported claims · Safety-critical - Model
You can also double the dose if symptoms are severe.
Unsafe or incorrect advice · Safety-critical
- Unsupported claims A confident claim with no support from the product information or the evidence.
- Unsafe or incorrect advice Dosing advice outside the product information.
Specialists matched to your therapy area review each answer and grade every issue by severity.
Pharma and life sciences, answered.
Anything else, just ask on a scoping call.
What kinds of AI do you evaluate for pharma and life sciences teams?
Any AI that produces clinical or medical content where accuracy matters: for example medical information and HCP-facing chatbots, literature review and evidence synthesis tools, medical writing assistants and models trained on clinical data. Specialist clinicians check outputs for accuracy, omissions, unsupported claims and safety-relevant errors.
Can you help with datasets as well as evaluation?
Yes. Specialist clinicians can annotate and review clinical text, imaging and other medical data, build reference test sets and provide expert feedback for models in development.
Do you run HCP market research, interviews or surveys?
Not as DeepLabel. Healthcare professional market research, including interviews and surveys, is run by our sister company BiteLabs through BiteLabs Pulse. If your project needs both, we can scope it with BiteLabs.
Which clinicians would review our work?
Specialists matched to your therapy area, drawn from the BiteLabs network of 30,000+ UK and US clinicians, supported by PhD data scientists. We confirm the right expertise for your project during scoping.
How do you handle confidential and clinical data?
We offer an NDA and put a data processing agreement in place on every project. Every reviewer signs a confidentiality and data protection agreement.
Does your review replace our medical, legal and regulatory sign-off?
No. Our findings provide clinical insight to support your own review and governance processes. They do not certify clinical safety, confer regulatory approval or replace formal validation requirements.
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