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LLM framework enhances accuracy in identifying adverse drug events

By PulseAugur Editorial · [1 sources] ·

A new research paper details a human-in-the-loop framework utilizing a retrieval-augmented, multi-agent large language model (LLM) to identify cutaneous immune-related adverse events (cirAEs) from clinical notes. This LLM-assisted workflow demonstrated improved accuracy and inter-rater agreement compared to manual review alone. The framework also significantly reduced the time required for reviewing adverse events, suggesting a scalable and transparent method for extracting such data. AI

IMPACT This framework demonstrates a scalable and accurate method for extracting critical medical data, potentially improving patient safety and drug development.

RANK_REASON Research paper detailing a novel application of LLMs in a specific medical domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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LLM framework enhances accuracy in identifying adverse drug events

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Research paper detailing a novel application of LLMs in a specific medical domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · 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 ·

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

    arXiv:2607.20428v1 Announce Type: new 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 manu…

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