RAG 完全指南：从概念到生产实践

To develop and evaluate JADE, a proof-of-concept retrieval-augmented generation (RAG) diagnostic assistive system, designed to enhance large language model (LLM) reasoning for jawbone lesion assessment. This study examined whether RAG improves diagnostic accuracy and stability compared with standalone LLMs and ORAD, a supervised learning-based system.

To evaluate whether large language models (LLMs) can autonomously synthesize existing literature and accurately extract prognostic variables for neonatal intraventricular hemorrhage (IVH) and its outcomes while assessing their capability for clinical feature ranking and risk stratification.

Traditional manual MedDRA coding in clinical data management (CDM) faces persistent challenges, including suboptimal site data quality, terminology complexity, low efficiency, inconsistent outcomes, frequent dictionary updates, and regulatory timeliness pressures-all of which hinder trial progress and compliance.