Blockchain-Enabled Trust for Adaptive Artificial Intelligence in Healthcare

Abstract

: The deployment of adaptive artificial intelligence (AI) in healthcare, where models continue to learn or retrain following initial deployment, introduces governance challenges that are foundationally distinct from those of static AI. In regulated clinical environments, these challenges manifest as four interrelated failure modes: concept drift and silent performance degradation, update poisoning and data integrity compromise, governance circumvention through ungoverned incremental modifications, and the collapse of model versioning and post-hoc auditability. This paper presents a blockchain-based trust architecture for adaptive healthcare AI comprising a four-layer system (user interaction, adaptive learning, blockchain governance, and IPFS storage), a version-controlled on-chain model registry, smart contract enforcement of update governance, and an explicit safety gate mechanism that activates a predetermined change control plan at the infrastructure level. The architecture extends a completed blockchain trust framework for static AI models and is motivated by a public cybersecurity survey in which blockchain was identified as the most frequently selected technology for improving healthcare system security following a data breach. The system is evaluated using the Architecture Trade-off Analysis Method (ATAM), with particular reference to the quality attributes of auditability, safety, scalability, and regulatory compliance.