RSNA review: LLM agents show promise for radiology but need multilayered evaluation
Michael_D_Moor · x · 2026-10-31
A review shared by RSNA argues that LLM-driven agentic systems show promise for complex radiology tasks, but require careful integration, multilayered evaluation, and attention to key challenges.
The accompanying figure lays out a full agent architecture: an LLM with procedural knowledge in a system framework, working memory (inputs, context, goals) and long-term memory (semantic + episodic), interacting through a protocol- and safeguard-constrained interface with an environment spanning HIS/RIS/PACS/EHR databases, general tools, radiology-specific tools (classification, segmentation, detection, measurements), and other AI agents. Authors include radiology AI researchers from Stanford and elsewhere.
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