Privacy-Preserving On-Device Generative AI for Personalized Healthcare Image Synthesis
Keywords:
privacy-preserving machine learning; generative adversarial networks; diffusion models; edge computing; healthcare image synthesis; federated learning; differential privacy; medical imaging; data governance; personalizationAbstract
Generative artificial intelligence is increasingly positioned as a transformative force in medical imaging, offering to produce patient-specific synthetic views that can support augmentation, simulation, education, and rare pathology representation. At the same time, personalized image synthesis creates acute privacy risks because generative models trained on sensitive health data can leak patient-specific visual information. On-device generative AI has emerged as a promising architectural response, preserving data locality while enabling personalization at the edge. This paper provides a system-level analysis of privacy-preserving on-device generative AI for healthcare image synthesis. It examines the structural relationships among learning paradigms, model architectures, edge hardware, privacy mechanisms, governance, fairness, and sustainability. We argue that technical privacy guarantees alone are insufficient without careful attention to deployment context, lifecycle management, and regulatory alignment. The paper discusses federated learning, differential privacy, model compression, hardware-aware generation, and adversarial threats such as membership inference and reconstruction. It further contrasts edge-native generative models with cloud-centric foundation models and considers the implications for energy use, bias amplification, auditability, and patient consent. By integrating perspectives from machine learning, systems engineering, and health policy, the analysis provides a comprehensive framework for designing and evaluating next-generation personalized medical image synthesis systems. The discussion concludes with future research directions that emphasize standardized evaluation, federated benchmarking, sustainable hardware co-design, and governance structures that preserve public trust.
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