Privacy-preserving ai for electronic health records: Balancing data utility and cyber defense

The Electronic Health Records (EHRs) is a notable contributor to the current clinical AI, which presents a paradox between the feasibility of the medical models and the requirement to guarantee the privacy of the patient and the safety of the system against the risk of cybercrimes. This is one of many clinically-inspired models, and this one will be capable of combining theory (information- and optimization-based privacy-utility descriptions), system architecture (federated learning, secure computation, trusted execution) and empirical evaluation design (benchmarks, attack suite, clinical utility measures). We provide mapping in the real-world healthcare threat agents and operational resource and suggest new healthcare-specific metrics on which the approaches might be compared. The initial tests show that the federated learning enhances the cross-site generalization and decreases the exposure to raw-data yet it is likely to suffer the gradient leakage and membership inference unless the extra safeguard of the differential privacy or secure aggregation. Homomorphic encryption and MPC have better confidentiality guarantees, are computationally expensive, whereas differential privacy is formally guaranteed, as well as having detrimental effects on clinical utility in uncommon outcomes. These judgments were made by using an adjusted set of available literature and suggest experimental process to measure the trade-offs. None of the techniques is panaceas. Rather, the optimal route to go is federated learning, which is differentially privatized and interrupted by appropriate and recommendable treatment and cryptography algorithms implemented in a robust governance and incident-response infrastructure that best secures and assures that clinical utility is preserved and offers adequate safeguards against the current attacks. We conclude by providing a plan of action to be appraised and feasible suggestions to the policy makers, the hospitals and the vendors.