Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events
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 unassisted manual review, the LLM-assisted workflow improved accuracy (F1 = 0.88 vs 0.77), inter-rater agreement measured by Cohen's kappa (kappa = 0.82 vs 0.50), and reduced average review time by approximately half. This framework pilots how LLMs can be applied to identify immune-related toxicities across organ systems and, more broadly, enable accurate, scalable, and transparent adverse event data extraction.
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[Submitted on 9 May 2026]
Title:Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events
View a PDF of the paper titled Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events, by Charles Lu and 16 other authors
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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 unassisted manual review, the LLM-assisted workflow improved accuracy (F1 = 0.88 vs 0.77), inter-rater agreement measured by Cohen's kappa (kappa = 0.82 vs 0.50), and reduced average review time by approximately half. This framework pilots how LLMs can be applied to identify immune-related toxicities across organ systems and, more broadly, enable accurate, scalable, and transparent adverse event data extraction.
Subjects:
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
Cite as: arXiv:2607.20428 [cs.CL]
(or arXiv:2607.20428v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20428
arXiv-issued DOI via DataCite
Submission history
From: Lirit Fuksman [view email] [v1] Sat, 9 May 2026 16:37:49 UTC (829 KB)
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