Category: AWS HealthLake

In this post, we build on that foundation by constructing specialized AI agents for each data modality along with a supervisor agent that orchestrates cross-modal analysis using Amazon Bedrock AgentCore and Strands Agents SDK. We also train predictive AI models with Amazon SageMaker AI to predict patient outcomes from multimodal features. To further explore the implementation details and get hands-on experience, refer to the accompanying code repository.

In this two-part blog series, we show how you can build agents that interact with multimodal HCLS data, making it easier for end users to query, explore, and ask questions of the data. We build on previous guidance for multimodal data analysis, which demonstrates how to store, query, and analyze clinical, genomic, and medical imaging data using purpose-built AWS services.

In this post, we describe a comprehensive, HIPAA-ready generative AI architecture for healthcare on Amazon Web Services (AWS) using a defense-in-depth approach. By layering compliance controls at multiple distinct levels, this architecture creates a system where no single point of failure compromises patient data protection, and each component that touches ePHI is independently auditable.

In this blog, you will gain detailed insight into the scientific rigor behind Amazon Connect Health, including our training methods (supervised fine-tuning, reinforcement learning, multi-agent collaboration), how we validate against real-world data (automated and manual evaluation, LLM agentic environments), and the evidence mapping and source tracing we provide for every output to reinforce safety and trust.

This blog post explains the vision behind how healthcare organizations can move from fragmented data to a connected, AI-ready future with AWS HealthLake

In this blog, learn how Electronic Medical Record (EMR) systems on AWS can evolve from static clinical repositories into the foundation for generative AI in healthcare—through two complementary strategies that extend existing systems with AI services or build on open standards like FHIR and openEHR.

In this post, we show how EHR companies, healthcare independent software vendors (ISVs), and tech-enabled provider organizations can use Amazon Connect Health to embed these capabilities into their existing workflows using a single, unified Software Development Kit (SDK).

It can be challenging to build a real-time agentic AI applications that interacts with AWS HealthLake datastores. This requires deep knowledge of fast healthcare interoperability resources (FHIR) operations and Amazon Web Services (AWS) SDK implementations. We’re excited to share an open-source Model Context Protocol (MCP) server. It streamlines healthcare data operations by providing natural language […]

As healthcare leaders and radiologists from around the world gather at the Radiological Society of North America (RSNA) Annual Meeting, Amazon Web Services (AWS) is proud to showcase how cloud technology is revolutionizing medical imaging. In an era where diagnostic precision and clinical efficiency are paramount healthcare organizations are leveraging the secure, scalable infrastructure of […]

At Amazon Web Services (AWS) we’re excited to announce the expansion of AWS HealthLake to the Canada (Central) Region, marking a significant milestone in our commitment to supporting healthcare organizations across Canada. This expansion enables the storage and transformation of health data using Fast Healthcare Interoperable Resources (FHIR) standards, while maintaining data residency within Canada, […]