An agentic LLM reasoning system reached 79.6% agreement with expert consensus on myeloma care questions from longitudinal records, outperforming iterative RAG and full-context baselines by 3.8-4.2 points with larger gains on complex cases.
Performance and improvement strategies for adapting generative large language models for electronic health record applications: a systematic review
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Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
An agentic LLM reasoning system reached 79.6% agreement with expert consensus on myeloma care questions from longitudinal records, outperforming iterative RAG and full-context baselines by 3.8-4.2 points with larger gains on complex cases.