Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events
2.60T1 sourcearXiv cs.MA
arXiv cs.MAoriginal source ↗
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.20428.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA retrieval-augmented, multi-agent LLM framework with human-in-the-loop was tested for detecting cutaneous immune-related adverse events from clinical notes. Compared with unassisted manual review, it raised F1 to 0.88 (vs 0.77), Cohen's kappa to 0.82 (vs 0.50), and roughly halved average review time.
Why it mattersA concrete multi-agent + human-in-the-loop case with measured gains, but confined to a niche clinical NLP task with limited transfer to general agentic workflows.
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