LLM framework enhances accuracy in identifying adverse drug events

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]

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• Cohen's kappa
• Human-in-the-Loop
• Large Language Model

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