Advisory
Bryan Tegomoh, MD, MPH advises the teams making consequential decisions about AI in medicine: health systems weighing adoption, companies building clinical AI, and the investors and frontier labs underwriting both. Engagements are independent and evidence-centered, held to the same standard as this handbook: what the evidence shows, what it does not, and what is at stake when a tool is wrong.
The chapters linked below are the public evidence base for the areas where independent review is most valuable.
Advisory Scope
For clinical and health system audiences, advisory work is most appropriate when a decision requires independent review of evidence, workflow, safety, and accountability:
- Clinical AI evaluation for product review, pilot selection, or vendor claims that need evidence-based interpretation
- Workflow integration for AI tools that must fit clinician practice, EHR processes, and implementation constraints
- Clinical AI safety and risk management for governance, monitoring, failure modes, and post-deployment oversight
- Liability and regulatory compliance for documentation-sensitive deployment, FDA status interpretation, and accountability planning
- Medical AI education for briefings, workshops, or curriculum design for physicians and implementation teams
Advisory work is independent. It does not imply endorsement of a product, organization, or public claim.
Typical engagements produce a decision-ready evidence map rather than a generic AI strategy. The work can include connected-triple citation review, FDA intended-use verification, claim-to-endpoint mapping, workflow and failure-mode analysis, pilot design, monitoring measures, and a concise record of unresolved risks. The scope is defined around the decision that must be made, the evidence available, and the clinical consequence of error.