Healthcare AI evaluation is a continuing process. The questions below help a hospital or digital health team connect model performance with patient safety and operational value.
Before procurement
- What clinical problem, intended use and patient population are defined?
- What evidence supports performance in comparable settings and subgroups?
- What data enters the system, where is it processed and how is access controlled?
- What happens when the output is wrong, missing or unavailable?
During deployment
- Who reviews each output, and who owns the final decision?
- How does the output enter the existing record and workflow?
- Which baseline measures will establish clinical and operational impact?
After launch
- Monitor quality, drift, incidents, user feedback and subgroup outcomes.
- Document model changes, oversight decisions and escalation paths.
- Reassess whether the system still improves care in its actual setting.
These questions are a starting point. Local clinical, legal and privacy teams should adapt them to each use case and jurisdiction.
Research and guidance