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arXiv · 2607.20428

Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events

Charles Lu·Olivia Burke·Debby Cheng·Adam Kashlan·Caitlyn Duffy·Zeyun Lu·Lirit Fuksman·Jin Ning Tian·Andrew Sedlack·Priya Katyal·Eudora Lee·Ralina Karagenova·Chuck Lin·Kun-Hsing Yu·Nicole LeBoeuf·Alexander Gusev·Yevgeniy R. Semenov
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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.

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Charles Lu, Olivia Burke, Debby Cheng, Adam Kashlan, Caitlyn Duffy, Zeyun Lu, Lirit Fuksman, Jin Ning Tian, Andrew Sedlack, Priya Katyal, Eudora Lee, Ralina Karagenova, Chuck Lin, Kun-Hsing Yu, Nicole LeBoeuf, Alexander Gusev, Yevgeniy R. Semenov. 2026-05-09. Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events. https://arxiv.org/abs/2607.20428

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