We bring our AI services to healthcare teams: preparing clinical and imaging data with clinical subject-matter experts, evaluating models against clinical reference standards, and building applications that respect patient privacy.
When this helps
- Clinical or imaging data needs expert labels before a model can learn from it.
- Patient data has to be de-identified before it can be used.
- A clinical AI tool needs evidence that it performs safely across patient groups.
- Clinicians want AI that works inside their existing workflow.
How would this work for you?
Answer three quick questions and get a short brief on how we'd approach your healthcare ai need.
What's included
- Clinical data de-identification and preparation
- Medical imaging annotation with clinical subject-matter experts
- Reference-standard building and model evaluation
- Healthcare AI applications and assistants
- Evidence support for regulatory submissions
How we do it
- ProtectDe-identify clinical and imaging data and agree the governance for how it is used.De-identified data and governance record
- LabelAnnotate records and images with clinical subject-matter experts, against written clinical guidelines.Clinically labelled dataset
- Set the referenceBuild reference standards with multiple expert readers, so models are judged against agreed ground truth.Reference-standard dataset
- EvaluateTest models for accuracy and consistency across patient groups, devices and sites.Clinical evaluation report
- ApplyBuild or integrate AI applications that fit clinical workflows, with people making the final decision.Validated application
How we keep quality high
- Clinical labelling is done or reviewed by people with matching clinical expertise.
- Performance is reported across patient groups, not only on average.
- Patient privacy is handled before any data is labelled or used for training.
- Evidence is organised to support ethics and regulatory review.
Why Qylis AI
Clinical experts in the loop
Clinical subject-matter experts write the guidelines and review the cases that matter.
One team, end to end
Data, training, applications and testing from one team, which matters when evidence must trace back to the data.
Privacy first
De-identification and access control come before any other work.
Evaluation built for scrutiny
Results are structured to support clinical and regulatory review.
Team profiles, tools, certifications and references are shared directly by our team, matched to your requirement.
Ways to work with us
Data pilot
De-identify and label a sample to prove the guidelines and the quality.
Programme
Data, reference standards and evaluation for a clinical AI product.
Ongoing support
Continuous labelling, re-evaluation and monitoring as the product evolves.
Every engagement follows the same rhythm: understand, assess, scope, deliver, with evidence against agreed criteria at each step.
Questions
Which kinds of healthcare data do you work with?
Clinical text, structured records and medical images. Each dataset is matched with reviewers who have the relevant clinical background.
How do you handle patient privacy?
Data is de-identified before labelling, access is limited to named people, and the work can happen inside your environment where required.
Do you help with regulatory submissions?
We prepare evaluation evidence and documentation that support submissions. The submission and the approval remain with you and the regulator.
What does Qylis AI's Healthcare AI offering cover?
We bring our AI services to healthcare teams: preparing clinical and imaging data with clinical subject-matter experts, evaluating models against clinical reference standards, and building applications that respect patient privacy. It includes clinical data de-identification and preparation; medical imaging annotation with clinical subject-matter experts; reference-standard building and model evaluation; healthcare ai applications and assistants; evidence support for regulatory submissions.
How does a Healthcare AI engagement start?
Understand: We start with the business requirement and the outcome you need to measure. Assess: We review the data, systems and constraints, and confirm where AI makes sense. Scope: We agree scope, acceptance criteria and a first deliverable you can judge. Deliver: We deliver in increments, with evidence against the agreed criteria at each step.
How do I discuss a Healthcare AI requirement with Qylis AI?
Send the requirement through the contact form on this page. A solution lead replies within one business day.
Talk to Qylis AI
Tell us the requirement. A solution lead replies within one business day.
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AI Data Services
Scan, clean, prepare, label and annotate training data, with subject-matter experts in the loop.
Model Training & SLMs
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AI Training Platforms
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AI Testing & Certification
Test AI solutions for accuracy, safety and conformity, and prepare them for external certification.