SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation

Georgi Grazhdanski, Vasil Vasilev, Sylvia Vassileva, Dimitar Taskov, Izabel Antova, Ivan Koychev, Svetla Boytcheva, SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation, Journal of Biomedical Informatics, Volume 170, October 2025, 104906

Build a shopping chatbot in four minutes with GraphDB Talk to Your Graph 2.0

In this video,Bob DuCharme demonstrates questions that you can ask a chatbot once it is set up in the first few minutes.

In this video, discover how to load data into GraphDB 10.8 and unleash the power of natural language queries with another “Talk to Your Graph 2.0” use case. The tutorial demonstrates a quick setup process, importing RDF data that includes product reviews, ratings, and an ontology. Watch as we configure a repository, enable full-text search, and create a natural language agent.

The video showcases how to interact seamlessly with your graph data, asking plain English questions and getting structured responses about products. You’ll see how SPARQL queries are automatically constructed and even explained, making it easier to understand your data. Whether you’re looking to analyze product categories, prices, or reviews, this video demonstrates the simplicity and efficiency of using GraphDB with minimal setup.

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SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation

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