Retrieval Augmented Generation Evaluation for Health Documents

Safe and trustworthy use of Large Language Models (LLM) in the processing of healthcare documents and scientific papers could substantially help clinicians, scientists and policymakers in overcoming information overload and focusing on the most relevant information at a given moment. Retrieval Augmented Generation (RAG) is a promising method to leverage the potential of LLMs while enhancing the accuracy of their outcomes. This report assesses the potentials and shortcomings of such approaches in the automatic knowledge synthesis of different types of documents in the health domain. To this end, it describes: (1) an internally developed proof of concept pipeline that employs state-of-the-art practices to deliver safe and trustable analysis for healthcare documents and scientific papers called RAGEv (Retrieval Augmented Generation Evaluation); (2) a set of evaluation tools for LLM-based document retrieval and generation; (3) a benchmark dataset to verify the accuracy and veracity of the results called RAGEv-Bench. It concludes that careful implementations of RAG techniques could minimize most of the common problems in the use of LLMs for document processing in the health domain, obtaining very high scores both on short yes/no answers and long answers. There is a high potential for incorporating it into the day-to-day work of policy support tasks, but additional efforts are required to obtain a consistent and trustworthy tool.

@article{ceresa2025_2505.04680,
  title={ Retrieval Augmented Generation Evaluation for Health Documents },
  author={ Mario Ceresa and Lorenzo Bertolini and Valentin Comte and Nicholas Spadaro and Barbara Raffael and Brigitte Toussaint and Sergio Consoli and Amalia Muñoz Piñeiro and Alex Patak and Maddalena Querci and Tobias Wiesenthal },
  journal={arXiv preprint arXiv:2505.04680},
  year={ 2025 }
}