TL;DR
This study aims to guide researchers and practitioners in designing secure, efficient, and privacy-preserving AI systems for healthcare by proposing a structured classification of privacy-preserving methods into four main categories: cryptographic approaches, decentralized learning methods, perturbation-based techniques, and hybrid models.
The analysis highlights how privacy-enhancing technologies offer benefits beyond anonymisation by embedding privacy-by-design values to support responsible innovation and protect sensitive patient data throughout the design, training, and validation of medical AI systems.
Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.
This review critically and systematically maps applications of PETs across the biomedical AI lifecycle in accordance with PRISMA-ScR guidelines and evaluates their technical trade-offs, deployment feasibility, and residual risks.
This systematic review surveys the neurological imaging data distinct features of high dimensionality, clinical sensitivity, and inter-institutional variability that require the application of FL with privacy-preserving methods protocols specifically to medical imaging in general.
A bio-inspired optimization framework based on the Discrete Grey Wolf Optimizer (DGWO) for effective optimized key generation in healthcare data sanitization processes and demonstrates that the proposed DGWO-based sanitization mechanism outperforms existing perturbation approaches in achieving a balanced tradeoff betwe...
Deployment simulations in a 5G-enabled edge–cloud environment demonstrate millisecond-level inference latency and reduced communication overhead, confirming the feasibility of secure, real-time, and sustainable AI-driven sepsis prediction in critical care environments.
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