AI for DoctorsInstituteExplore coursesUseful AI must also be accountable.
A practical centre for clinicians and healthcare leaders learning to evaluate AI without surrendering privacy, equity or professional judgment.
A baseline for responsible clinical AI.
Adapt these principles to the risk, intended use and jurisdiction of each system.
- 01
Define the intended use
State the patient population, user, decision, workflow and safe fallback before assessing a tool.
- 02
Validate in context
Do not assume that vendor or research performance will transfer to a different population or care setting.
- 03
Protect patient information
Use only approved systems and the minimum necessary data; never place identifiable patient information into an unapproved public AI service.
- 04
Preserve human judgment
Specify who reviews outputs, how uncertainty is handled and when users must override or escalate.
- 05
Test for inequity
Examine subgroup performance, access barriers, language, disability and the possibility of unequal downstream harm.
- 06
Communicate transparently
Explain what the system does, its limitations and how it affects decisions whenever that information is relevant to users or patients.
- 07
Assign accountability
Name owners for clinical safety, privacy, cybersecurity, procurement, incident response and change control.
- 08
Monitor over time
Track drift, incidents, overrides, outcome changes and software updates after deployment.
- 09
Plan for failure
Maintain downtime procedures and a safe non-AI workflow for unavailable, uncertain or degraded systems.
- 10
Educate continuously
Train teams in privacy, bias, hallucination, evidence appraisal, workflow effects and patient communication.
Do not proceed when…
- The intended use or responsible clinical owner is unclear.
- Patient data would enter an unapproved environment.
- Evidence does not represent the intended patient population.
- Users cannot understand, challenge or safely ignore the output.
- There is no monitoring, incident response or downtime plan.