Applying the power of retrieval-augmented generation in health care

Turn Generative AI Into a Reliable Decision Tool

Generative AI holds promise for health care – but without transparency and trusted data, its outputs can be difficult to validate. Retrieval-augmented generation (RAG) addresses this challenge by grounding GenAI responses in curated, verifiable information.By combining traditional analytics, GenAI and trusted enterprise data, RAG enables health care organizations to move beyond experimentation and use AI with confidence in real-world decision workflows. 

General-purpose AI models are often trained on broad internet data, and the quality and provenance varies widely. This increases risk, drives manual verification and limits adoption in regulated health care environments.RAG prioritizes intentionally selected, vetted and often proprietary data sources, delivering:

• Greater transparency and explainability.
• More accurate, context-aware responses.
• Faster insights without sacrificing trust.

When implemented within a governed decision workflow – including large language models (LLMs), natural language processing and agentic AI – RAG supports responsible, scalable use of GenAI in health care. 

This e-book explores three real-world health care use cases showing how retrieval-augmented generation is being applied today to improve decision-making and operational efficiency. 

Learn how retrieval-augmented generation helps health care organizations move from AI experimentation to trusted, scalable impact.