The growing adoption of machine learning (ML) in healthcare offers transformative opportunities for diagnosis, prognosis, and personalized treatment. However, the deployment of ML models in clinical environments introduces substantial risks related to patient data privacy, regulatory compliance, and operational transparency. This paper presents a privacy-first MLOps framework designed to address these challenges by integrating federated learning, differential privacy, and secure multiparty computation into the machine learning lifecycle. The framework enables collaborative model development across healthcare institutions without sharing raw patient data, while also ensuring rigorous protection against inference attacks and data leakage. Through an architectural and experimental analysis, we demonstrate how the proposed system supports secure data flows, continuous model integration, and privacy-preserving deployment. A series of simulations using realistic clinical datasets shows that the system maintains strong predictive performance even under strict privacy budgets. Key components include privacy-aware CI/CD pipelines, role-based access control, immutable audit logs, and real-time tracking of privacy budgets. The framework aligns with key data protection regulations, such as HIPAA and GDPR, providing a scalable and trustworthy foundation for real-world clinical AI applications. This work contributes a practical and adaptable blueprint for deploying machine learning in healthcare environments that demand both technological sophistication and ethical integrity. It also highlights current limitations and outlines future research directions to enhance interpretability, regulatory alignment, and cross-institutional collaboration in privacy-preserving AI.

privacy-preserving machine learning, federated learning, MLOps, healthcare AI, differential privacy, regulatory compliance.

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