Hospitals, care providers, and pharmaceutical companies are deploying AI faster than oversight can keep pace – in diagnosis, triage, drug development, and patient care. Every automated decision here carries a different weight than in any other industry.
If AI is making decisions about a patient’s care, are they accurate, fair, and accountable?
AI governance deserves a closer look.
A triage algorithm deprioritises a patient because the training data underrepresented people like them.
A clinical decision support tool recommends a treatment pathway – but no one on the care team can explain why, and no one questions it.
A pharmaceutical AI model identifies a promising drug candidate, trained on historical trial data that excluded entire populations from the original research.
KPI Matrix – Get Started with the Checklist
Most AI risk doesn’t show up in a model’s accuracy score – it shows up in the questions (such as the ones listed below).
This checklist gives you a fast, honest read on where your organisation actually stands: not in theory, but in the decisions your AI is making right now. Work through each one – if you can’t answer confidently, that’s exactly where to start.
KPI
Meaning
Example
Decision Analysis
Can you explain it?
Can a clinician or patient understand why an AI made a diagnostic or triage recommendation?
Inbuilt Bias
Is it fair across groups?
Could patients of different age, ethnicity, or background receive different outcomes from the same model?
Checks and Balances
Is a human actually watching?
Is there meaningful clinical oversight before an AI recommendation reaches a patient?
Model Design Control
Do you know what changed?
Do you know when a model’s behaviour changes after deployment, mid-trial, or post-approval?
Corporate Accountability
Could you defend it tomorrow?
Can you defend an automated clinical or research decision to a patient, regulator, journalist, or your Board?
These five are where every serious AI ethics conversation starts, not where it ends. Behind each one sits a deeper set of variables we work through to build governance models sophisticated enough for how your organisation actually operates.
Let us help you! Start with our Assessment Services
We work with healthcare institutions to identify ethical risks before they become regulatory findings, customer complaints, or reputational issues.
Pillars of Ethical AI 