Evidence map›Paper›PMID 39572076›Full record

ReviewNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2025

Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee.

Wim Van Biesen, Jadranka Buturovic Ponikvar, Monica Fontana, Peter Heering, Mehmet S Sever, Simon Sawhney, Valerie Luyckx

Abstract readReview
In one paragraph

Review in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Wim Van BiesenDepartment of Nephrology, University Hospital Gent, Gent, Belgium.ORCID 0000-0002-4782-5224
Jadranka Buturovic PonikvarUniversity Medical Centre Ljubljana, Division of Internal Medicine, Department of Nephrology, Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Slovenia.
Monica FontanaEuropean Renal Association, Headquarters, Parma, Emilia-Romagna, Italy.
Peter HeeringKFH, Solingen General Hospital. Solingen, Germany. Dept of Nephrology and Hypertension, Univ of Cape Town, Cape Town, South Africa.
Mehmet S SeverIstanbul School of Medicine, Nephrology department, Millet Caddesi, Capa-Istanbul, Turkey.
Simon SawhneyAberdeen Centre for Health Data Sciences, University of Aberdeen, Aberdeen, UK.
Valerie LuyckxDepartment of Public and Global Health, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage the collection of big data of our patients. We identify some ethical reflections on the use of big data, and their importance and relevance. Furthermore, we balance advantages and pitfalls and discuss requirements to make legitimate and ethical use of big data possible.  The collection, organization, and curation of data come upfront in the pipeline before any analyses. Great care must therefore be taken to ensure quality of the data at this stage, to avoid the 'garbage in garbage out' problem and suboptimal patient care as a consequence of such analyses.  Access to the data should be organized so that correct and efficient use of data is possible. This means that data must be stored safely, so that only those entitled to do so can access them. At the same time, those who are entitled to access the data should be able to do so in an efficient way, so as not to hinder relevant research.  Analysis of observational data is itself prone to many errors and biases. Each of these biases can finally result in provision of low-quality medical care. Secure platforms should therefore also ensure correct methodology is used to interpret the available data. This requires close collaboration of a skilled workforce of experts in medical research and data scientists. Only then will our patients be able to benefit fully from the potential of AI and big data.

Indexed as

Artificial IntelligenceBig DataBiomedical ResearchEthics CommitteesKidney DiseasesNephrologyHumansartificial intelligencebig datamachine learningobservational trialreal world evidence

Identifiers

PMID39572076
PMCPMC11879022

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.