The paper outlines an architectural framework that builds measurable trust in clinical AI through evidence trails, human oversight, tiered escalation, and staged autonomy rather than model accuracy alone.
Cecchi, and Pattie Maes
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From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy
The paper outlines an architectural framework that builds measurable trust in clinical AI through evidence trails, human oversight, tiered escalation, and staged autonomy rather than model accuracy alone.