Health AI expert - NHS-facing clinical AI

Healthcare AI expertise
grounded in clinical experience.

I help NHS-facing teams connect clinical AI ideas to workable clinical workflows, validation evidence and safety governance. My work combines NHS experience, Clinical Safety Officer practice and product development through Spheno Labs.

Discuss healthcare AI → Official profile
NHSClinical experience
CSOClinical safety accountability
SphenoClinical AI product builder
Short answer

How can I help your team?

I work with NHS-facing teams on clinical AI workflows, validation planning and safety governance. I draw on my NHS oral and maxillofacial surgery experience, contracted CSO work and product development through Spheno Labs.

The scope runs from defining the clinical problem to planning evidence, reviewing risks and setting up governance after deployment. Read the clinical and professional background →

Relevant experience

Healthcare AI needs more than model knowledge.

The hard part is converting AI into a safe clinical workflow: intended use, validation, clinical risk management, buyer evidence, DTAC, DCB 0129 and ongoing governance.

Clinical reality

Doctor with NHS experience

My advice is grounded in NHS clinical workflow experience, referral pressure, handover risk, documentation burden and how clinicians actually use decision support.

Safety accountability

Clinical Safety Officer

Healthcare AI going into NHS workflows needs clinical safety evidence, hazard analysis and a named accountable clinician. I provide CSO support for suitable products.

Product building

Spheno Labs

I founded Spheno Labs to build clinician-in-the-loop AI products. That development work informs my advice on product scope, evidence and implementation.

What I advise on

Health AI work where clinical judgement matters.

Clinical AI strategy where AI helps, where it does not, and what the measurable clinical outcome is Clinician-in-the-loop design escalation paths, confidence display, uncertainty and overrides Validation planning intended use, reference standard, cohort, metrics and acceptance thresholds AI clinical safety automation bias, dataset shift, model change and workflow hazards DCB 0129 and DTAC named CSO, hazard log, CRMF, CSCR and release memo evidence Post-go-live governance change control, monitoring, incident review and model-update decisions
Working together

The short answer.

I can help you define a clinical use case, plan the evidence, review hazards and set up governance. The scope depends on your product and team; formal regulatory, legal and specialist technical decisions may need additional expertise.

Related pages

Explore the support your team needs.

Service

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Clinical AI implementation, workflow, validation and safety support for NHS-facing teams.

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How to assess workflow experience, validation discipline, AI clinical safety, governance and confidentiality.

Read guide → Validation

Clinical AI validation

Evidence planning for intended use, claim limits, reference standards and NHS deployment governance.

Read → Governance

Healthcare AI governance

Accountability, validation evidence, clinical safety and post-go-live controls for NHS-facing clinical AI.

Read → AI / SaMD route

AI medical-device consultant

Intended purpose, validation, DCB 0129, DTAC and governance alignment for NHS-facing AI products.

AI medical-device page → Safety

AI clinical safety expert

AI hazard analysis, model failure modes, SaMD DCB 0129 and CSO sign-off for suitable products.

AI safety page →

Need clinician-led healthcare AI advice?

Send the clinical problem, intended users, patient group, current evidence and target NHS setting. The first answer is whether AI is the right tool at all.

Contact → Clinical and professional background