PRIVACY-PRESERVING PHENOTYPE-AWARE SYNTHETIC INTELLIGENCE FOR FAIR AND RELIABLE MEDICAL IMAGING

Abstract

            Privacy regulations, demographic imbalances, and uncertainty in clinical deployment constrain the development of medical imaging AI. We propose a unified phenotype-conditioned synthetic intelligence framework that integrates (i) demographic and pathology conditioning, (ii) differential privacy, (iii) distribution-shift correction, (iv) demographic bias equalization, and (v) uncertainty calibration. Unlike prior pipelines that add safety and fairness post-hoc, our architecture embeds these objectives into the generative and diagnostic optimization. Using public benchmarks in a controlled simulation study, we show improvements in accuracy, fairness, and privacy risk with calibrated uncertainty. This Study contributes to deployable architecture, mathematically grounded objectives, and a reproducible protocol for regulated clinical AI.