AI-Driven Privacy-Preserving Techniques in US Healthcare Cybersecurity: A Narrative Review

Abstract

The United States healthcare sector grapples with rising cybersecurity threats. Ransomware and data breaches expose millions of protected health information (PHI) records each year, while artificial intelligence (AI) has advanced analytics and supported clinical decisions and tools. AI amplifies both vulnerabilities and protections, but traditional safeguards often struggle or fail to support collaborative model development with stringent HIPAA and HITECH rules. Privacy-preserving machine learning (PPML) techniques offer potential solutions to this tension. This narrative review draws together peer-reviewed literature from 2020 to 2026 on AI-driven privacy-preserving techniques like federated learning, differential privacy, homomorphic encryption, secure multi-party computation, and blockchain-AI hybrids applied to US healthcare cybersecurity. These tools allow decentralized training, encrypted operations, and auditable partnerships that curb re-identification, inference attacks, and centralized data risks. Key findings highlight federated learning’s maturity in multi-institutional applications, differential privacy’s solid defenses for group-level analysis, and the promise of hybrids to overcome individual limitations such as computational overhead and expansion barriers. However, persistent challenges include resource demands, potential bias amplification, adversarial vulnerabilities, and limited real-world longitudinal evidence. The review calls for uniform testing standards, quantum-proof designs, and policy boots to speed uptake. Such methods strengthen privacy alongside function, paving the way for reliable AI use that protects patients, cuts breach damage, and promotes digital health innovation in an increasingly threatened ecosystem.