Use case
Define the problem, users, output, supported decision and criterion for success.
02HEALTHCARE AI · GOVERNANCE
Artificial intelligence does not enter healthcare alone.
Decisions carry it in.
Data, models and automation matter. The chosen problem, accountability, oversight and the way a system enters real work matter even more.
Process and accountability come before the algorithm.
Healthcare AI is not one category. An administrative system, documentation support, a research model and a tool embedded in clinical workflow have different functions and risks. Treating them alike means governing none of them.
My approach begins with the use case and moves through data, people, organisation, security, literacy and the full life cycle. The objective is not to make a process look intelligent. It is to make it more intelligible and responsible.
Data and context
Understand provenance, representativeness, quality and limits before asking a model for an answer.
Oversight
Design the human ability to verify, disagree, intervene and document.
AI literacy
Train by role: boards, management, IT, professionals and users do not face the same risk.
Governance
Inventory, ownership, monitoring, incidents, updates and the ability to stop the system.
Adoption
Bring technology into real work without turning people into extras in the algorithm’s story.
A FRAMEWORK, NOT A FORMULA
Health AI Readiness Map
Problem → data → context → oversight → accountability → outcome. Every step must be intelligible and verifiable.
Assess readiness ↗Governable
AI
AI
PROBLEM
DATA
CONTEXT
OVERSIGHT
OUTCOME
A conversation, not a form