This article proposes a practical framework for engineering measurable trust in clinical AI systems, moving beyond subjective impressions of model performance. It emphasizes integrating evidence, human supervision, and staged autonomy within a multi-layered architecture to ensure safety and accountability in healthcare applications.

Trustworthy AI systems increasingly rely on probabilistic computation, shifting performance bottlenecks from arithmetic to memory, which must deliver both data and randomness. This paper introduces a unified data-access perspective, treating deterministic access as a limiting case of stochastic sampling, to analyze and address these new memory challenges.