Good judgement.
Built into the system.
I help health AI and digital care teams design, evaluate and govern systems that are clinically safer and usable in real care.
Where I can
be useful.
AI doctors, clinical copilots, telehealth, preventative health platforms and clinical decision-support products.
From a focused safety review to ongoing clinical leadership, the work starts with your product, its intended use and the decisions your team needs to make.
Clinical AI safety audits
A structured review of your AI product, model outputs, clinical content, escalation logic, user-facing claims and safety boundaries.
This may include:
- Review of model responses or clinical content
- Identification of unsafe or misleading outputs
- Red-flag and escalation pathway review
- Clinical scope boundary review
- Human oversight and workflow review
- Risk-prioritised remediation plan
Best for teams that already have a product, prototype or clinical workflow and want a focused external review.
AI doctor and clinical copilot evals
Design and review of evaluation systems for AI doctors, clinical copilots, symptom engines and health AI products.
This may include:
- Benchmark case design
- Synthetic patient cases
- Training and test case libraries
- Clinical reasoning rubrics
- Red-flag safety criteria
- Longitudinal and preventative health evals
- Failure-mode taxonomies
- Regression testing for model or prompt changes
Best for teams that need to know whether their AI system is clinically reliable across realistic patient scenarios, not just polished demo cases.
Clinical governance frameworks
Design of practical governance systems for health AI products and digital care workflows.
This may include:
- Clinical accountability models
- Human-in-the-loop review pathways
- Escalation and handover logic
- Audit trail requirements
- Clinical risk registers
- Safety review workflows
- Clinical policy architecture
- Incident and feedback loops
Best for teams preparing to scale, raise capital, enter regulated markets or work with clinical partners.
Regulatory and product translation
Practical translation of health AI, SaMD, clinical decision support and digital health governance requirements into product and operational decisions.
This may include:
- Product risk classification support
- Review of clinical claims and user-facing language
- SaMD and CDSS boundary analysis
- TGA, FDA and EU-style regulatory risk mapping
- Documentation gaps and governance requirements
- Founder or executive briefing memos
This is not a substitute for formal legal advice. It is designed to help teams understand how clinical, regulatory and product risks interact before they commit to a build or launch pathway.
Fractional clinical AI leadership
Ongoing advisory support for teams that need senior clinical, product, safety or governance input without hiring a full-time clinical AI lead.
This may include:
- Weekly or fortnightly advisory sessions
- Clinical product review
- AI output review
- Governance and policy design
- Safety eval oversight
- Founder and executive support
- Clinical implementation strategy
- Support preparing for investors, partners or regulators
Best for early-stage or scaling teams that need senior clinical AI judgement embedded into the product cycle.
Defined work.
Useful outputs.
A clinical AI safety audit. An evaluation framework. A governance model. A risk review memo. Ongoing support for the decisions that need senior clinical judgement.
All work is subject to confidentiality, conflict checks and appropriate boundaries around current roles and client information.
Read the complete advisory scope