Neuro-Oracle distills longitudinal MRI changes into trajectory vectors via a 3D Siamese encoder, retrieves similar cases, and generates LLM-based prognoses, achieving AUC 0.834-0.905 on a resection-type proxy task versus 0.793 for single-timepoint baseline.
Large language models encode clinical knowledge.Nature, 620 (7972):172–180
2 Pith papers cite this work. Polarity classification is still indexing.
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RAG4Outcome is a retrieval-augmented multimodal framework for prognostic prediction in chronic osteomyelitis using imaging reports, structured records, and unstructured notes.
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Neuro-Oracle: A Trajectory-Aware Agentic RAG Framework for Interpretable Epilepsy Surgical Prognosis
Neuro-Oracle distills longitudinal MRI changes into trajectory vectors via a 3D Siamese encoder, retrieves similar cases, and generates LLM-based prognoses, achieving AUC 0.834-0.905 on a resection-type proxy task versus 0.793 for single-timepoint baseline.
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RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis
RAG4Outcome is a retrieval-augmented multimodal framework for prognostic prediction in chronic osteomyelitis using imaging reports, structured records, and unstructured notes.