This use case develops a generative AI assistant embedded within the BioImage Archive to help users efficiently discover and analyse imaging data. It combines natural language interfaces with powerful AI models for tasks like cell segmentation, making complex data exploration and analysis accessible even to non-experts.
Generative AI-Powered assistant for data discovery and analysis
RI: Euro-Bioimaging ERIC
Challenge
Research Infrastructures (RIs) generate vast, complex datasets and tools that can be difficult for users—especially those without advanced technical backgrounds—to navigate and analyse. Generative AI-based assistants have the potential to revolutionise user interaction by enabling intuitive access to data and embedded analysis features such as cell segmentation and counting. This use case aims to implement such a tool in the BioImage Archive, enhancing both user experience and operational efficiency.
Target
To implement a generative AI assistant that simplifies dataset discovery and enables in-platform advanced image analysis for end users, regardless of their technical expertise.
Development Steps
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Design and integrate an AI-powered chat assistant directly into the BioImage Archive interface.
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Adapt AI models for seamless integration with the assistant, supporting both data discovery and complex image analyses.
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Enable user-friendly features for dataset navigation and analysis (e.g., cell morphology) without requiring external tools or expertise.
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Test and evaluate the assistant’s performance and scalability across different dataset sizes and use cases.
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Deploy via the DEP infrastructure, optimising resource usage for performance and environmental sustainability.
Relevance
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Scientific users needing accessible tools for bioimage exploration and analysis
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RI user support teams seeking to streamline user interaction and reduce routine support workload
Expected Impact
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Democratises access to advanced imaging analysis by lowering technical barriers
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Improves user satisfaction and research productivity
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Reduces routine workload for RI support teams, enabling focus on high-value services
