Define the intended use precisely. A detection tool, diagnostic system and treatment adviser have different functions and risks.
Evaluate clinically relevant performance. Accuracy alone is insufficient; missed disease, false positives and consequences for patient management matter.
Require independent validation. Testing should represent the populations, hospitals and equipment where the system will actually operate.
Examine performance across patient groups. An acceptable average can conceal important weaknesses in under-represented populations.
Make limitations visible. Users need to understand suitable inputs, excluded situations and the degree of human oversight required.
Distinguish regulatory status from general endorsement. Authorisation concerns a specified product and intended use, not every claim made about it.
Monitor after implementation. Changes in data, workflow or software can alter performance and require reassessment.
Plan updates and failure responses. Hospitals need clear processes for incident reporting, version control and withdrawal when necessary.
Protect patient information. Data handling and cybersecurity are part of clinical safety.
Take-home: Safe clinical AI requires validation, clear accountability and continued monitoring throughout its lifecycle.