Generative AI-Powered Clinical Intelligence: Safely Driving Better Outcomes

The article discusses how AI21's Contextual Answers technology can help healthcare organizations gain insights from unstructured medical data like clinical notes while adhering to strict guardrails to prevent risks like hallucination (false information). It uses a retrieval-augmented generation (RAG) approach to answer questions based on the information in the provided text.

Specifically, the article covers:

• The challenge of extracting insights from unstructured clinical notes, and the risks of incorrect information from language models
• An overview of retrieval-augmented generation (RAG) and how it helps language models provide answers grounded in the provided text
• The architecture and workflow for using AI21's Contextual Answers on Amazon SageMaker
• A walkthrough demonstrating how Contextual Answers answers questions based on a mock clinical note, and responds "None" when the answer is not present
• Potential next steps like integrating with Amazon Lex for conversational AI and Amazon Kendra for enterprise search
• Conclusion on the value of using Contextual Answers for responsible, reliable insights from unstructured medical data