Evidence map›Paper›PMID 41140864›Full record

ArticleData in brief2025

RAGCare-QA: A benchmark dataset for evaluating retrieval-augmented generation pipelines in theoretical medical knowledge.

Jovana Dobreva, Ivana Karasmanakis, Filip Ivanisevic, Tadej Horvat, Dimitar Kitanovski, Matjaz Gams, Kostadin Mishev, Monika Simjanoska Misheva

Abstract read
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In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jovana DobrevaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia.
Ivana KarasmanakisDepartment of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
Filip IvanisevicDepartment of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
Tadej HorvatDepartment of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
Dimitar KitanovskiFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia.
Matjaz GamsDepartment of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
Kostadin MishevFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia.
Monika Simjanoska MishevaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Healthcare AIMedical education, knowledge assessmentMedical Knowledge BaseMultiple choice questionsRetrieval-augmented generationTheoretical Medicine

Identifiers

PMID41140864
PMCPMC12553001

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.