ClinicalRAG: Automating Pharmaceutical Label Quality Control with Hierarchical RAG and Large Language Models
Qiaohui Zhou, Zhongliang Zhou, Michael Johnson, Michelle Ngo, Federico Ferrari, Junshui Ma
Abstract
Every pharmaceutical product must be accompanied by a comprehensive label that delineates its indications, usage, dosages, and side effects, essential for safe medication practices. Traditionally, creating drug labels is labor-intensive and dependent on manual quality checks. Recent advancements in Large Language Models (LLMs) offer a promising avenue to streamline this process. In this paper we introduce ClinicalRAG, an automated labeling quality control pipeline that integrates LLM with hierarchical Retrieval Augmented Generation that allows to cross-check every statement in the drug label document. ClinicalRAG enhances the reliability of automated drug labeling by systematically reducing hallucination risks, achieving an accuracy of 96.1% in internal validation. With user-friendly interface, our pipeline aims to support pharmaceutical company in drug approval and expedite patients' access to new treatments.
BibTeX
@article{Zhou_Zhou_Johnson_Ngo_Ferrari_Ma_2025, title={ClinicalRAG: Automating Pharmaceutical Label Quality Control with Hierarchical RAG and Large Language Models}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35384}, DOI={10.1609/aaai.v39i28.35384}, abstractNote={Every pharmaceutical product must be accompanied by a comprehensive label that delineates its indications, usage, dosages, and side effects, essential for safe medication practices. Traditionally, creating drug labels is labor-intensive and dependent on manual quality checks. Recent advancements in Large Language Models (LLMs) offer a promising avenue to streamline this process. In this paper we introduce ClinicalRAG, an automated labeling quality control pipeline that integrates LLM with hierarchical Retrieval Augmented Generation that allows to cross-check every statement in the drug label document. ClinicalRAG enhances the reliability of automated drug labeling by systematically reducing hallucination risks, achieving an accuracy of 96.1% in internal validation. With user-friendly interface, our pipeline aims to support pharmaceutical company in drug approval and expedite patients’ access to new treatments.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhou, Qiaohui and Zhou, Zhongliang and Johnson, Michael and Ngo, Michelle and Ferrari, Federico and Ma, Junshui}, year={2025}, month={Apr.}, pages={29736-29738} }