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Analysis

This ArXiv paper explores the application of Large Language Models (LLMs) and supervised learning in identifying incidentalomas that necessitate follow-up, a critical task in radiology. The multi-anatomy focus suggests a comprehensive evaluation, potentially impacting clinical workflows.
Reference

The research focuses on the automated identification of incidentalomas that require follow-up.