Proving the First Read: Why Cryptographic Sequential Disclosure Is Necessary for Trustworthy AI-Assisted Clinical Decisions

Recent mock-juror research demonstrates that when a radiologist reviews a case independently before seeing AI output, perceived liability for missed findings drops substantially compared to a single simultaneous read. This sequential workflow, in which the clinician forms a judgment before AI exposure, thus carries measurable evidentiary value. However, no current clinical information system guarantees that the sequence actually occurred. A clinician's claim of independent pre-AI assessment is taken on faith, it cannot be independently verified, cryptographically substantiated, or defended against retroactive modification. This paper argues that the evidentiary value of sequential reading collapses without a mechanism to prove the sequence was authentic. The same verification gap renders post-deployment appropriate-reliance metrics mathematically undefined, because the pre-AI human judgment required for their computation is unrecoverable from standard clinical records. We propose a cryptographic sequential disclosure architecture modeled on the Certificate Transparency framework that restructured the Web public key infrastructure after the 2011 DigiNotar compromise. The architecture uses a commit-reveal protocol with SHA-256 hash chaining, server-enforced AI disclosure gates, hardware-bound identity authentication, RFC 3161 timestamping and W3C PROV-compatible provenance metadata to produce tamper-evident evidence that is independently verifiable without vendor software or server access. Implications for malpractice defense, regulatory compliance and human-AI interaction measurement are discussed. Patent pending.