Agentic LLMs autonomously execute complex neuro-radiological workflows like glioma segmentation and multi-timepoint response assessment by directing off-the-shelf tools, without any model training.
Nature medicine29(8), 1930–1940 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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Linear probes recover evidence grades from LLM activations (median AUROC 71.8) across 22 models but the models' stated grades perform at chance level and the signal is largely lexical.
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Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Agentic LLMs autonomously execute complex neuro-radiological workflows like glioma segmentation and multi-timepoint response assessment by directing off-the-shelf tools, without any model training.
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The strength of clinical evidence is recoverable from language model representations but not from their stated grades
Linear probes recover evidence grades from LLM activations (median AUROC 71.8) across 22 models but the models' stated grades perform at chance level and the signal is largely lexical.