Clinical data summarization using generative AI involves leveraging advanced algorithms to extract, analyze, and condense vast amounts of medical information into concise, actionable insights.This technology employs natural language processing (NLP) to understand and interpret clinical narratives, enabling the generation of summaries that highlight crucial patient data, trends, and outcomes.By automating the summarization process, generative AI reduces the time and effort required by healthcare professionals to review patient records, enhances decision-making accuracy, and improves patient care. It ensures that critical information is readily accessible, facilitating more efficient and informed clinical workflows.
Clinical data summarization using generative AI involves leveraging advanced algorithms to extract, analyze, and condense vast amounts of medical information into concise, actionable insights. This technology employs natural language processing...
In this session, Leann Chen will introduce GraphRAG, a method that integrates knowledge graphs with large language models (LLMs) to enhance Retrieval-Augmented Generation (RAG) systems. Graph RAG can address challenges...
Building an AI prototype is easy and quick these days. Building production-grade systems is a different story. How do you keep moving quickly and run robustly? In this talk, we...
Earnings calls are a critical source of information for investors, analysts, and stakeholders, providing insights into a company’s financial performance, strategies, and future outlook. However, the sheer volume and complexity...
Everybody loves vector search and enterprises now see its value thanks to the popularity of LLMs and RAG. The problem is that prod-level deployment of vector search requires boatloads of...
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