Cryptographic Attestation of Medical AI: Bridging the Trust Gap in Nuclear Medicine via Proof of Inference (Preprint)

Abstract

BACKGROUND The rapid integration of deep learning into nuclear medicine promises to revolutionize precision oncology but faces a critical "trust gap." As AI models become "black boxes," clinicians struggle to verify the integrity of individual diagnostic inferences, leaving systems vulnerable to adversarial attacks and silent model drift. OBJECTIVE This formative evaluation proposes and validates an in-silico proof-of-concept for a blockchain-agnostic Proof of Inference (PoI) protocol. The objective is to establish a standard of Computational Integrity for AI-assisted workflows in nuclear medicine without exposing proprietary model weights or patient privacy. METHODS he PoI protocol leverages Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), specifically the Groth16 proof system. An in-silico feasibility study was conducted using a 1.2-million-parameter U-Net model on synthetic 128×128 Ga-68 PSMA-11 PET slices. Proof generation and verification latencies were benchmarked using an NVIDIA A100 GPU and a standard CPU, respectively. RESULTS The architectural analysis demonstrates that the protocol successfully offloads computational burden to the prover (cloud server). In our empirical benchmarking, cryptographic proof generation required 28.81 seconds per inference. Crucially, client-side verification of the proof was completed in 448.59 milliseconds, demonstrating that cryptographic attestation can be integrated into existing PACS viewers with sub-second, clinically acceptable latency. CONCLUSIONS The proposed PoI protocol provides a feasible forensic support layer for medical AI. By shifting clinical trust from institutional reputation to deterministic cryptographic assurance, this infrastructure creates a tamper-evident audit trail essential for algorithmic accountability in decentralized healthcare environments.