{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T16:23:58Z","timestamp":1793291038693,"version":"4.5.0"},"reference-count":166,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Artificial intelligence (AI), encompassing machine learning, deep learning and generative AI, is poised to redefine nephrology by enabling earlier detection, more precise risk stratification and workflow-integrated clinical decision support across the spectrum of kidney disease. This state-of-the-art review synthesizes emerging applications of AI in acute kidney injury (AKI), chronic kidney disease (CKD), dialysis and kidney transplantation, with attention to clinical integration, real-world validation, workflow implementation and translational challenges. In AKI, predictive models trained on high-frequency electronic health record data and intensive care unit telemetry have demonstrated strong performance in forecasting critical events, yet translation into routine clinical workflows remains limited. In CKD, machine learning tools support progression risk stratification and phenotype clustering, with the potential to inform individualized surveillance and therapy. AI-enabled dialysis management systems optimize ultrafiltration, anemia control and vascular access surveillance, while generative AI and large language models offer novel capabilities for clinical documentation, triage and patient education. In transplantation, AI applications span organ allocation, dynamic graft monitoring and digital pathology-assisted rejection classification, with validated tools such as the iBox system gaining regulatory recognition. Implementation challenges include data heterogeneity, bias, interpretability, regulatory uncertainty and workflow integration. Looking ahead, multimodal integration of imaging, pathology and multi-omics data may support biologically informed precision nephrology. Reinforcement learning, digital twins and ambient intelligence are emerging as adaptive decision-support paradigms rather than autonomous care systems. Regulatory frameworks are evolving to accommodate adaptive algorithms, underscoring the need for clinician engagement in model development, validation and deployment. As AI matures from pilot to practice, nephrologists who embrace and help shape these tools will lead the transition toward a more personalized, efficient and equitable future for kidney care.<\/jats:p>","DOI":"10.1093\/ckj\/sfag004","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T12:43:09Z","timestamp":1767789789000},"source":"Crossref","is-referenced-by-count":27,"title":["Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration"],"prefix":"10.1093","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9954-9711","authenticated-orcid":false,"given":"Wisit","family":"Cheungpasitporn","sequence":"first","affiliation":[{"name":"Mayo Clinic Division of Nephrology and Hypertension, Department of Medicine, , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ambarish","family":"Athavale","sequence":"additional","affiliation":[{"name":"Division of Nephrology, University of California , San Diego, La Jolla, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9930-3575","authenticated-orcid":false,"given":"Lama","family":"Ghazi","sequence":"additional","affiliation":[{"name":"University of Alabama at Birmingham Department of Medicine, , Birmingham , AL,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2184-3683","authenticated-orcid":false,"given":"Kianoush B","family":"Kashani","sequence":"additional","affiliation":[{"name":"Mayo Clinic Division of Nephrology and Hypertension, Department of Medicine, , Rochester, MN ,","place":["USA"]},{"name":"Mayo Clinic Division of Pulmonary and Critical Care Medicine, Department of Medicine, , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-1538","authenticated-orcid":false,"given":"Tiago","family":"Colicchio","sequence":"additional","affiliation":[{"name":"Mayo Clinic Division of Nephrology and Hypertension, Department of Medicine, , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6873-8712","authenticated-orcid":false,"given":"Jay L","family":"Koyner","sequence":"additional","affiliation":[{"name":"University of Chicago Section of Nephrology, Department of Medicine, , Chicago, IL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Alabama at Birmingham Department of Medicine, , Birmingham, AL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim 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