Graph neural networks offer a unifying artificial intelligence framework to model related objects, ranging from tissue architecture and geometrical relationships to patient similarity and multi-organ networks. Applications of this technology in nephrology include computational representation of kidney histopathology and modelling of the complex interactions between organs in kidney diseases.
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Acknowledgements
P.B.’s work is supported by the German Research Foundation (DFG, projects 322900939 and 445703531), European Research Council (ERC Consolidator Grant No. 101001791), the Federal Ministry of Education and Research (BMBF, STOP-FSGS-01GM2202C), and the Innovation Fund of the Federal Joint Committee (Transplant.KI, No. 01VSF21048). We thank all participants of the Kármán Conference: Computational Nephropathology – New Frontiers and Challenges in Precision Nephropathology held at RWTH Aachen University, Germany, in 2026 for many interesting discussions and perspectives.
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Schaub, M.T., Boor, P. Graph neural networks for computational nephrology. Nat Rev Nephrol (2026). https://doi.org/10.1038/s41581-026-01059-z
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DOI: https://doi.org/10.1038/s41581-026-01059-z
