20.500.12556/DiRROS-25043 RAGCare-QA a benchmark dataset for evaluating retrieval-augmented generation pipelines in theoretical medical knowledge RAGCare-QA: a benchmark dataset for evaluating retrieval-augmented generation pipelines in theoretical medical knowledge The paper introduces RAGCare-QA, an extensive dataset of 420 theoretical medical knowledge questions for assessing Retrieval-Augmented Generation (RAG) pipelines in medical education and evaluation settings. The dataset includes one-choice-only questions from six medical specialties (Cardiology, Endocrinology, Gastroenterology, Family Medicine, Oncology, and Neurology) with three levels of complexity (Basic, Intermediate, and Advanced). Each question is accompanied by the best fit of RAG implementation complexity level, such as Basic RAG (315 questions, 75.0 %), Multi-vector RAG (82 questions, 19.5 %), and Graph-enhanced RAG (23 questions, 5.5 %). The questions emphasize theoretical medical knowledge on fundamental concepts, pathophysiology, diagnostic criteria, and treatment principles important in medical education. The dataset is a useful tool for the assessment of RAG- based medical education systems, allowing researchers to fine-tune retrieval methods for various categories of theoretical medical knowledge questions. medical education retrieval-augmented generation theoretical knowledge multiple-choice questions medicinsko izobraževanje ocena znanja teoretično znanje vprašanja z več možnimi odgovori true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2025 The Author(s). Neznano 2026-01-08 11:32:25 2026-01-08 11:32:25 2026-01-09 03:52:12 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Soavtorji iz Slovenije: Ivana Karasmanakis, Filip Ivanisevic, Tadej Horvat, Matjaž Gams; Opis vira z dne 8. 1. 2026; str. 1-11 Vol. 63, [article no.] 112146 Dec. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-06-29 2025-10-01 2025-10-09 004.8 2352-3409 10.1016/j.dib.2025.112146 263916803 https://www.sciencedirect.com/science/article/pii/S2352340925008674 1 5620a3d5-ec7d-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=37583 RAZ_Dobreva_Jovana_2025.pdf RAZ_Dobreva_Jovana_2025.pdf The dataset is available on Hugging Face Hub: https://huggingface.co/datasets/ChatMED-Project/RAGCare-QA. 1 095F423B47FF0E0B8227C1229E909D6F 1a4fe0f59d398f23d1e497dd058642eb95b035261f8399f30d9d9b20e222d9a6 9edb7610-ec7d-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=37584 Institut Jožef Stefan 0 0 0