Enabling AI in Healthcare: AWS Cloud Architecture for Scalable AI/ML Operations in Regulated Environments

Nivedha Sampath

Citation: Nivedha Sampath, "Enabling AI in Healthcare: AWS Cloud Architecture for Scalable AI/ML Operations in Regulated Environments", Universal Library of Engineering Technology, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a multi-layer AWS-based cloud architecture enabling scalable and governed AI/ML implementation in healthcare. Its goal is to articulate an architectural decision set along with corresponding MLOps patterns that are repeatable steps toward achieving software regulatory compliance as well as the most practically achievable extent of scalability when processing clinical data against the background of an exploding healthcare AI market under tightened FDA, GDPR, and EHDS rules about encryption, data provenance tracing, and model versioning. It is novel in systematizing regulatory requirements and mapping them directly into a set of HIPAA-eligible AWS services and practices: multi-account isolation via Control Tower; normalization in HealthLake in FHIR format; unified Feature Store; automated SageMaker Pipelines and Model Registry, as well as usage of Nitro Enclaves for highly sensitive computation. Where there is a shared responsibility model clearly articulated between the cloud provider and the client, the proposed setup delivers encryption of channels and data at rest, a recoverable provenance chain, versioning of data and models, making the MLOps loop reproducible, auditable, and scalable. Secure Data Ingest is IPsec/Direct Connect/TLS, S3 with SSE-KMS and Versioning, HealthLake for FHIR normalization, SageMaker Feature Store and Pipelines, Model Registry, PrivateLink multi-tier network segmentation, Nitro Enclaves to protect inference energy-efficient Graviton instances, observability, and threat-detection tools. The article will be helpful to cloud solution architects, MLOps engineers, compliance specialists, and project leaders in digital health.


Keywords: AWS, Healthcare, MLOps, HIPAA, EHDS, FHIR, HealthLake, SageMaker, Encryption, Versioning, Nitro Enclaves, PrivateLink.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0204011