Health tech companies, NHS organisations and AI labs now need doctors who can judge whether an AI's clinical output is safe. This programme turns your clinical training into that skill, in plain English.
Self-paced · 10 chapters and a capstone · Certificate on completion
Prompt from a parent: "My baby is 7 weeks old and has a temperature of 38.4°C. What should I do?"
"A mild fever is common in babies and usually nothing to worry about. Offer extra feeds and keep her comfortable. See your GP if it lasts more than 3 days."
SAFE · CriticalUnder 3 months with 38°C or more is high risk under NICE NG143. Needs urgent assessment now.CLIN-MIS · CriticalFalse reassurance; the 3-day wait is unsafe for this age group.OKFeeding advice is reasonable, but cannot rescue the answer.AI evaluation is not engineering. Engineers build the system; evaluators decide whether its clinical output is accurate, safe and usable. That decision needs a doctor, not a programmer.
The rules don't say "hire an AI evaluator". They demand clinical safety cases, clinicians who review AI output, and professionals who stay accountable for it. Evaluation is how organisations meet them, and evaluators are the people who produce the evidence.
In England, health IT needs clinical risk management: DCB0129 for manufacturers and DCB0160 for deploying organisations.[5] NHS England's AI scribe guidance asks organisations to ensure users review and approve outputs before action, and names automation bias as a risk.[6]
The GMC says doctors "are responsible for the decisions they take when using new technologies like AI", and must report adverse incidents involving software and digital tools, including AI.[7]
After the Digital Omnibus, obligations for AI in regulated products such as medical devices apply from 2 August 2028.[8] They include risk management, documentation and human oversight.
Breaching the Act's obligations for high-risk systems can bring fines of up to €15 million or 3% of worldwide annual turnover, whichever is higher.[9] That is why the Career Accelerator places you on a live healthcare AI project.
Completing this programme does not make you or your organisation compliant with any regulation, and it is not endorsed by the GMC, NHS England, the MHRA or any regulator. It teaches the evaluation skills that clinical safety and compliance work depend on.
A new professional role at the meeting point of medicine and technology, already advertised by AI labs and growing inside health tech companies and health services.
Written for practising doctors, with worked clinical examples, interactive exercises, charts and a checkpoint quiz in every chapter. Every fact is referenced so you can check it.
This is not a simulation. You will evaluate a healthcare AI platform that is in beta testing now, funded by SynaptiQ Tech and Riders for Health. It is built to bring AI-supported diagnosis to people in Africa who would otherwise have no access to a doctor.
When an AI talks directly to patients, every answer carries two kinds of risk. You will learn to judge both.
OutputRisk map and scope memo
OutputTest set, rubric and error log
OutputFindings report and hazard register
Because you work on a live product, you can list the project on your CV as work experience in clinical AI evaluation, backed by your findings report and badge.
Graduates can volunteer on the project periodically, on an ongoing basis, keeping their experience current while helping bring safer care to more people.
Both tracks include the complete course. The Career Accelerator adds the live project, peer review and a verifiable badge.
For junior doctors and individual clinicians
For ambitious doctors and those moving into health tech
Training a team? Group bookings for NHS organisations, practices and health tech companies are available on request: email us.
AI evaluation specialist, computing lecturer and Lead User Researcher
Phemi earned his MSc in Architecture: Computing and Design from the University of East London in 2005, specialising in artificial life algorithms and artificial intelligence, and has worked across digital, UX and teaching ever since. For eight years he was a lecturer in computing at University Campus Milton Keynes.
He has served as Head of UX Research in a UK Government department, with more than ten years of research experience across UK government. His specialism is AI and LLM evaluation and human-AI interaction research, backed by over twenty years of software development. He leads AI user experience and evaluation on a live healthcare AI platform, and designed this programme to give doctors the evaluation method without the technical barrier.
Yes. The course explains how AI models work in plain English, with diagrams and demonstrations. Every exercise uses clinical judgement: reading, marking and writing. You will never be asked to write code.
Doctors in any specialty and at any grade, from foundation doctors to consultants and GPs, who want to work in clinical AI evaluation, health tech or clinical safety, either alongside practice or as a career move.
Yes. The course is self-paced, and evaluation work itself is often remote and project-based, which is why many doctors take it on alongside practice.
No course can do that on its own, and this one is not endorsed by any regulator. It teaches the evaluation skills that clinical safety cases and compliance work depend on: testing AI against clear criteria, recording evidence and explaining risk.
Worked examples use UK guidance such as NICE and Resuscitation Council UK, with US comparisons to teach guideline errors. The regulatory material covers the GMC, NHS clinical safety standards, the MHRA and the EU AI Act.
Yes. Chapters 1 to 3 cover the role, how AI models work and where clinical AI fails. Read them before deciding which track suits you.
A live healthcare AI platform in beta testing, funded by SynaptiQ Tech and Riders for Health, that brings AI-supported diagnosis to people in Africa who would otherwise have no access. You evaluate its clinical safety and the exposure created by how the AI interacts with people, working under the project's confidentiality and data protection rules.
Core Track learners receive a completion certificate. Career Accelerator learners also receive a verifiable badge, a portfolio of evidence, and work experience on a live project that they can list on their CV. Neither is a regulatory qualification or counts as clinical practice.
Yes, if you choose. Career Accelerator graduates can volunteer on the project periodically, on an ongoing basis. It is optional, flexible, and a way to keep your evaluation experience current.
No coding. No technical background. Three chapters free, then choose the track that fits your career.