从零理解 LLM 与 Agent

We developed and evaluated a training-free, large language model (LLM) multiagent framework, consisting of role-prompted LLM instances, that simulates hospital-style team diagnosis for Alzheimer's disease, improving interpretability and generalizability for multimodal data.

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.

Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most existing evaluations lack methodological transparency, rely on closed-source models, and show limited standardization in performance and safety assessment.

Background/Objectives: