Secure model deployment strategies for machine learning in regulated healthcare environments
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Secure model deployment strategies for machine learning in regulated healthcare environments
Veerendra Nath Jasthi
veerendranathjasthi@gmail.com
Abstract— The introduction of machine learning (ML) in the medical field has the opportunity to transform clinical diagnostics, monitoring, and therapy prescription. But when these types of models are used in the controlled medical setting, it brings several issues concerning the information privacy, legal compliance and the integrity of the model. The following paper dwells on ways to create secure deployment systems of ML models in healthcare facilities by considering the regulatory documents regarding the healthcare field (HIPAA, GDPR, and FDA guidelines) and outlines the architectural and procedural measures to be taken to guarantee compliance and trust. Our approach to methodology utilized all of these aspects of privacy-preserving methods, secure model hosting, access controls, auditability, and explainability. Robustness of different strategies is evaluated through a clear comparison of real-world deployments and performance. Finally, the paper draws roadmap of a scalable deployment of secure ML in healthcare in line with the legal and ethical standards.
Keywords— Machine Learning, Secure Deployment, Healthcare Regulations, HIPAA, GDPR, Model Governance, Privacy-Preserving ML, Model Interpretability, Compliance, Federated Learning.