Evidence map›Paper›PMID 41907824›Full record

ArticleKidney international reports2026

Renal Clinical Study Participants Support Data Sharing and Use of Artificial Intelligence.

Verónica Aramendía-Vidaurreta, Leyre Garcia-Ruiz, Maite Aznárez-Sanado, Malene Aastrup, Michela Bozzetto, Paolo Brambilla, Rebeca Echeverria-Chasco, Esben S S Hansen, Larisa Micu, Jose María Mora-Gutierrez and 17 more

Abstract read
In one paragraph

Article in Kidney international reports, 2026. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

27 authors.

Verónica Aramendía-VidaurretaDepartment of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.
Leyre Garcia-RuizDepartment of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.
Maite Aznárez-SanadoSignicat, Spain.
Malene AastrupThe MR Research Centre, Aarhus University, Aarhus, Denmark.
Michela BozzettoDepartment of Bioengineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
Paolo BrambillaRadiology Unit, ASST Papa Giovanni XXIII, Bergamo, Italy.
Rebeca Echeverria-ChascoDepartment of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.
Esben S S HansenThe MR Research Centre, Aarhus University, Aarhus, Denmark.
Larisa MicuAdvanta, Siemens, 15 Noiembrie Bvd, Brasov, Romania.
Jose María Mora-GutierrezIdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain.
Siria PasiniDepartment of Bioengineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
Anish RajComputer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Steffen RinggaardThe MR Research Centre, Aarhus University, Aarhus, Denmark.
Anika StrittmatterComputer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Giulia VillaDepartment of Bioengineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
Ioana UrdeaAdvanta, Siemens, 15 Noiembrie Bvd, Brasov, Romania.
Gorka BastarrikaDepartment of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.
Niels Henrik BuusDepartment of Renal Medicine, Aarhus University Hospital, Denmark.
Nuria Garcia-FernandezIdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain.
Nicholas M SelbyCentre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, School of Medicine, University of Nottingham, UK.
Matias TrilliniCentro di Ricerche Cliniche per le Malattie Rare "Aldo e Cele Daccò," Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
Susan T FrancisSir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK.
Lucian-Mihai ItuAdvanta, Siemens, 15 Noiembrie Bvd, Brasov, Romania.
Christoffer LaustsenThe MR Research Centre, Aarhus University, Aarhus, Denmark.
Frank G ZöllnerRICORS2040 (Kidney Disease), Pamplona, España.
Anna CaroliDepartment of Bioengineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
Maria A Fernández-SearaDepartment of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Kidney diseases are a global health concern. Sharing data from renal clinical studies and the use of artificial intelligence (AI) can advance research in these pathologies. Understanding participant attitudes toward these practices is essential for ethical and effective implementation. This study explored their perspectives using a structured survey, based on the hypothesis that participants held positive views of data sharing and AI use. Methods: The survey was distributed to European clinical centers. It included 42 questions assessing attitudes toward data sharing, AI use, and collecting explanatory variables. Data were analyzed using descriptive statistics, Cronbach's alpha for internal consistency, statistical tests to evaluate the effect of selected variables, regression to identify predictors of attitudes, and principal component analysis (PCA) to explore underlying factors. Results: Participants expressed positive views about data sharing (mean score: 0.52 ± 0.24) and AI use (0.33 ± 0.24) on a normalized scale from -1 (negative) to 1 (positive). Internal consistency was high. Regression analysis identified institutional trust and family income as predictors of attitudes toward data sharing. Health status, institutional trust, and AI knowledge were predictors of attitudes toward AI, indicating that those with better health, more institutional trust, and higher AI knowledge had a more favorable view. PCA revealed 2 distinct dimensions for data sharing (benefits and concerns) and 1 for AI (concerns). Conclusion: Renal clinical study participants generally support data sharing and AI use, although views on AI are less favorable in patients than healthy volunteers. Institutional trust shapes attitudes in both areas, highlighting the relation between both domains.

Indexed as

artificial intelligencedata sharinginstitutional trustpatient perspectivequantitative surveyrenal clinical study

Identifiers

PMID41907824
PMCPMC13022613

What OpenQuestion holds

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Registered trials

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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.