Blockchain-based Smart Contract Frameworks for Secure AI Healthcare Systems: A Systematic Review of Privacy-Preserving Methodologies

Abstract

Artificial Intelligence (AI) is changing the healthcare industry by automating the process of medical records, diagnostic, and clinical decision-making. Nevertheless, the growing dependence on data-driven systems is of concern in terms of patient privacy, data integrity, and system security. To deal with these challenges, blockchain technology, in combination with smart contracts, can be used as a decentralized and transparent solution. The paper is a systematic review of the current developments in smart contract frameworks based on blockchain technologies and safe AI-driven healthcare systems. The review addresses four key aspects, which include data privacy preservation, secure information exchange, decentralized access management, and AI-blockchain integration. The recent technologies like federated learning, zero-knowledge proofs, and deep learning-based blockchain validation are examined in terms of their contribution to the improvement of patient confidentiality and system-level security. The paper also assesses a suggested multi-layered infrastructure combining artificial intelligence processing, authorized blockchain, and role-based smart contracts. The comparison indicates that the federated learning approach with permissioned blockchain has an optimal security- versus-computational efficiency ratio. The review establishes that smart contract automation has a substantial potential to benefit healthcare data governance, but notes that scalability, interoperability and energy efficiency issues remain a challenge. This overall overview confirms the potential of blockchain and smart contracts to facilitate dependable, most open, and efficient healthcare frameworks that may meet the security and privacy requirements of the contemporary healthcare setting.