用AI助手提升药企FDA合规效率，实时提供可追溯的决策支持。
GMPilot: An Expert AI Agent For FDA cGMP Compliance
- 基于法规与检查记录构建知识库，结合RAG与ReAct框架实现智能推理。
- 模拟检查中显著提升质量人员响应速度与专业性，支持可验证决策。
- 适合制药企业质量管理部门，探索AI在严监管领域的应用路径。
制药行业面临质量管理成本高、响应慢、知识碎片化等挑战。本文提出GMPilot，一个面向FDA cGMP合规的领域专用AI代理。该系统基于精心整理的法规和历史检查数据知识库，采用检索增强生成（RAG）与推理-行动（ReAct）框架，为质量管理人员提供实时、可追溯的决策支持。在模拟检查场景中，GMPilot展现出通过结构化知识检索和可验证的法规及案例支持，显著提升质量人员响应速度与专业水准的能力。尽管在法规覆盖范围和模型可解释性方面仍有不足，GMPilot仍为利用智能化手段改善制药领域质量管理决策提供了可行方案，是高度监管行业中专用AI应用的典型案例。
原文摘要 · Abstract (English)
The pharmaceutical industry is facing challenges with quality management such as high costs of compliance, slow responses and disjointed knowledge. This paper presents GMPilot, a domain-specific AI agent that is designed to support FDA cGMP compliance. GMPilot is based on a curated knowledge base of regulations and historical inspection observations and uses Retrieval-Augmented Generation (RAG) and Reasoning-Acting (ReAct) frameworks to provide real-time and traceable decision support to the quality professionals. In a simulated inspection scenario, GMPilot shows how it can improve the responsiveness and professionalism of quality professionals by providing structured knowledge retrieval and verifiable regulatory and case-based support. Although GMPilot lacks in the aspect of regulatory scope and model interpretability, it is a viable avenue of improving quality management decision-making in the pharmaceutical sector using intelligent approaches and an example of specialized application of AI in highly regulated sectors.
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