A multi-agent framework uses natural language to generate and execute Python code for dynamic bibliometric analysis including networks, clustering, and automated reports.
Leveraging large language models for data analysis automation
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LLaMA 3.1 extracts visual rating scores from Dutch neuroradiology reports with 87-96% balanced accuracy but only 66-80% on numerical counts, with few-shot prompting raising the latter to 81-92%.
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AI-Augmented Bibliometric Framework: A Paradigm Shift with Agentic AI for Dynamic, Snippet-Based Research Analysis
A multi-agent framework uses natural language to generate and execute Python code for dynamic bibliometric analysis including networks, clustering, and automated reports.
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Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
LLaMA 3.1 extracts visual rating scores from Dutch neuroradiology reports with 87-96% balanced accuracy but only 66-80% on numerical counts, with few-shot prompting raising the latter to 81-92%.