From big data to smart decisions: artificial intelligence in kidney risk assessment

Devon A Barnes; Luiz Ladeira; Rosalinde Masereeuw

doi:10.1038/s41581-025-00962-1

From big data to smart decisions: artificial intelligence in kidney risk assessment

Devon A Barnes, Luiz Ladeira, Rosalinde Masereeuw^*

^*Corresponding author for this work

University of Liege

Research output: Contribution to journal › Comment/Letter to the editor › Academic › peer-review

Abstract

Artificial intelligence approaches that link patient data with chemical-induced kidney injury patterns are revolutionizing nephrotoxicity risk assessment. Substantial progress has been made in the development of integrated approaches that leverage big data, molecular profiles and toxicological understanding to identify at-risk patients, provide insights into molecular mechanisms and advance predictive nephrology.

Original language	English
Article number	9256
Journal	Nature Reviews. Nephrology
DOIs	https://doi.org/10.1038/s41581-025-00962-1
Publication status	E-pub ahead of print - 7 Apr 2025

Bibliographical note

Publisher Copyright:
© Springer Nature Limited 2025.

Funding

The authors\u2019 work was performed in the context of the ONTOX project ( https://ontox-project.eu/ ), which has received funding from the European Union\u2019s Horizon 2020 Research and Innovation programme under grant agreement no. 963845, as well as the Virtual Human Platform for Safety Assessment (VHP4Safety) project, funded by the Netherlands Research Council (NWO) Netherlands Research Agenda: Research on Routes by Consortia (NWA-ORC 1292.19.272). ONTOX is part of the ASPIS project cluster ( https://aspis-cluster.eu/ ).

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Virtual Human Platform for Safety Assessment
Netherlands Research Council
Horizon 2020 Framework Programme	963845
NWA-ORC	1292.19.272

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10.1038/s41581-025-00962-1

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From big data to smart decisions: artificial intelligence in kidney risk assessment

Abstract

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