Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study

Background: Adverse drug events (ADEs) pose significant public health challenges and economic burdens. While substantial ADE information is documented in unstructured clinical notes, its extraction remains difficult due to semantic complexity. Large language models (LLMs) offer promising text comprehension capabilities but are often hindered by domain-specific hallucinations. Objective: This study aims to evaluate the effectiveness of retrieval-augmented generation (RAG) in improving the identif

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ethics.ai (10 August 2026), “Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study,” evidence record 18087, https://ethics.ai/record/18087 (originally published by JMIR (Journal of Medical Internet Research)).

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