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Abstract This study evaluated a retrieval-augmented, multi-agent large language model (LLM)–driven, human-in-the-loop framework for detecting cutaneous immune-related adverse events (cirAEs) from clinical notes. Compared with the unassisted manual-review group, the LLM-assisted group demonstrated higher accuracy (F1 = 0.88 vs 0.77) and inter-rater agreement measured by Cohen’s κ (κ = 0.82 vs 0.50), and reported an approximately one-third shorter average review time.
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