Formal Reliability Analysis of Multi-Layer Deterministic Verification in Clinical AI: A Fault Tree Approach to Quantifying Hallucination Probability

Abstract

This monograph establishes the quantitative reliability bound for the anti-hallucination architecture of the DeepSensi Cognitive Medical OS. The derived bound is an engineering-grade safety characterization of a multi-layer verification cascade based on IEC 61025 (Fault Tree Analysis) and IEC 61508-6 (Functional Safety - Common-Cause Failure) standards. We compute the worst-case probability that an undetected, unsupported assertion or fabricated diagnostic hypothesis survives all independent probabilistic and deterministic barriers. Under adversarial assumptions and applying a beta-factor for common-cause degradation, we derive a worst-case bound of 3.23 x 10^-6 per assertion, providing a 3.1-fold safety margin relative to the IEC 61508 SIL-4 low demand threshold.