Exploring the Potential of Retrieval Augmented Generation for Question Answering in Radiology: Initial Findings and Future Directions

Studies in health technology and informatics, vol. 327, pp. 863–867

Abstract

This study explores the application of Retrieval-Augmented Generation (RAG) for question answering in radiology, an area where intelligent systems can significantly impact clinical decision-making. A preliminary experiment tested a naive RAG setup on nice radiology-specific questions with a textbook as the reference source, showing moderate improvements over baseline methods. The paper discusses lessons learned and potential enhancements for RAG in handling radiology knowledge, suggesting pathways for future research in integrating intelligent health systems in medical practice.

Authors 4

1. Yongli Mou Aachen Chair of Computer Science 5

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Germany

2. Robert Malte Siepmann Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Germany

3. Daniel Truhnn Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Germany

4. Sulayman K. Sowe Aachen Chair of Computer Science 5

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Germany