A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
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Abstract (arXiv)
We explore the potential for combining generative AI with grammar-based visualizations for biomedical data discovery. In our prototype, we use a multi-agent system to generate visualization specifications and apply filters. These visualizations are linked together, resulting in an interactive dashboard that is progressively constructed. Our system leverages the strengths of natural language while maintaining the utility of traditional user interfaces. Furthermore, we utilize generated interactive widgets enabling user adjustment. Finally, we demonstrate the potential utility of this system for biomedical data discovery with a case study.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 19 Sep 2025.
Builds on
25 of 48 references.
References · 25
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- 2022Towards Natural Language-Based Visualization Authoringcited by 22
13 more
- 2023AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Makingcited by 21
- 2024Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Waycited by 21
- 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
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- 2025InterChat: Enhancing Generative Visual Analytics using Multimodal Interactionscited by 13
- 2024Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Educationcited by 12
- 2024DracoGPT: Extracting Visualization Design Preferences from Large Language Modelscited by 12
- 2022Sporthesia: Augmenting Sports Videos Using Natural Languagecited by 11
- 2024PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasetscited by 9
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- 2019A Natural-language-based Visual Query Approach of Uncertain Human Trajectoriescited by 4
- 2020Sentifiers: Interpreting Vague Intent Modifiers in Visual Analysis using Word Co-occurrence and Sentiment Analysiscited by 2
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