Article

End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study

2026-02-25

Abstract excerpt

<h4>Background</h4> Although deep learning models have improved individual PET analysis, image processing and quantification tasks, end-to-end automation from raw DICOM to quantitative clinical reporting remains limited, particularly in heterogeneous real-world settings. <h4>Methods</h4> As a proof-of-concept, an autonomous large language model (LLM)-orchestrated multi-tool agent for end-to-end PET/CT interpreta...

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Literature Corpus work: e1e86f27-cb72-57b9-89ba-442a097908c2
DOI: 10.64898/2026.02.21.26346798

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