Start with the evidence
Healthcare AI should not be considered without examining the evidence supporting it.
That includes understanding the intended use, study population, comparator, performance measures, limitations, validation approach, and relevance to the setting in which the system may be used.
But a publication cannot answer every implementation question.
From study conditions to local practice
An organisation still needs to consider how the evidence relates to its own circumstances:
Does the local population resemble the population studied?
Does the proposed use match the evaluated intended use?
How will the output enter the clinical workflow?
Who reviews or acts on it?
What happens when a professional disagrees with the result?
How will limitations be explained to users?
What should be monitored after deployment?
Who has authority to intervene if performance or use becomes concerning?
These questions do not weaken evidence-based decision-making. They are part of applying evidence responsibly.
Evidence continues after deployment
Real-world use creates further information about performance, adoption, workload, unintended consequences, and professional behaviour.
That information should not be treated as an informal substitute for structured evaluation. It can help organisations identify what needs closer examination, which assumptions were incomplete, and where additional evidence may be required.
Research and practice therefore need an ongoing relationship.
Researchers can help implementation teams understand what the available evidence supports. People working in practice can identify questions, conditions, and outcomes that future research needs to address.
The useful question is rarely only, “Does the model perform?”