There is overwhelming evidence from academic research and industry benchmarks that domain-specific and task-specific large language models outperform general-purpose LLMs across multiple dimensions: Accuracy, veracity, human preference, and cost.This session presents the results of a double-blind study, in which medical doctors compared John Snow Labs’ healthcare-specific LLMs with OpenAI’s GPT-4o across four popular medical language understanding tasks: Medical text summarization, across a variety of patient notes and report types Open-ended medical question answering, testing out-of-the-box general medical knowledge losed-ended question answering – extracting specific information from a given patient note, such as a patient’s primary diagnosis or disease stage Closed-ended biomedical research – understanding a given research paper abstract
There is overwhelming evidence from academic research and industry benchmarks that domain-specific and task-specific large language models outperform general-purpose LLMs across multiple dimensions: Accuracy, veracity, human preference, and cost. This...
Explore the transformative potential of Generative AI in the world of digital publishing. This case study leverages the CrewAI framework and Google’s Gemini model to create sophisticated, engaging eBooks with...
Unifying large language models (LLMs) and knowledge graphs (KGs) can address the shortcomings of LLMs such as lack of factual knowledge, hallucinations and lack of interpretability. Integrating LLMs with knowledge...
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...
Demos
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