What explainable clinical AI should make visible

A confidence score alone is rarely enough. Clinicians need to understand what information was considered, what the output is intended to mean, and what it cannot establish.

Explanations should be shaped around the people using the system and the decisions they are responsible for. A technical feature ranking may help a researcher, while a clinician may need a clear summary of relevant observations and uncertainty.

Performance can change across settings, populations, and collection conditions. Reporting those limits is part of making a model understandable. Systems should also identify missing or poor-quality inputs instead of presenting false precision.

Clinical AI can support structured assessment and research. It should not be presented as a diagnosis or as a substitute for a qualified professional. Accountability requires human oversight, validation, and a clear route to question or correct an output.