ReviewJournal of the American Society of Nephrology : JASN2026
Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.
Review in Journal of the American Society of Nephrology : JASN, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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.
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.
Who cites it
26 citing papers in PubMed.
- Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal dialysis.Renal failure · 2026Article
- Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.Renal failure · 2026Article
- Necroptosis and the RIPK1-RIPK3-MLKL pathway in chronic kidney disease: mechanisms, crosstalk, and therapeutic opportunities.Renal failure · 2026Review
- Single-cell transcriptomics and machine-learning reveal M1 macrophage-driven progression from minimal change disease to focal segmental glomerulosclerosis.Renal failure · 2026Article
- A novel and two recurrent UMOD mutations in autosomal dominant tubulointerstitial kidney disease (ADTKD): a case series and literature review.Renal failure · 2026Review
- Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD.Renal failure · 2026Article
- Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.Renal failure · 2026Observational
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.Renal failure · 2026Article
- Vitamin D deficiency, multimorbidity, and mortality in stage 2 cardiovascular-kidney-metabolic (CKM) Syndrome: evidence from the NHANES 2001-2018 cohort.Renal failure · 2026Article
- Article
- A nine-gene diagnostic model for IgA nephropathy based on multi-cohort machine learning: integrating gene expression and immunohistochemical validation.Renal failure · 2026Article
- Hemodialysis versus peritoneal dialysis and diastolic blood pressure variability: volume-dependent cardiovascular risks in maintenance dialysis patients.Renal failure · 2026Article
- Bioinspired hybrid optimisation and deep belief neural networks for early chronic kidney disease detection: an explainable clinical AI framework.Renal failure · 2026Article
- Predicting long-term allograft outcomes in kidney transplant recipients using a machine learning approach: a 5-year retrospective cohort study.Renal failure · 2026Article
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.Renal failure · 2026Article
- A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.Renal failure · 2026Article
- Enhancing Education for Nephrologists in Fellowship and Practice.Journal of the American Society of Nephrology : JASN · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence-A Roadmap for Workflow-Integrated Care.Journal of clinical medicine · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Artificial intelligence (AI) is rapidly transforming the delivery of kidney care through predictive analytics, machine learning, deep learning, and generative AI technologies. To meet this challenge, the American Society of Nephrology convened an AI Workgroup to provide a framework for the responsible use of AI in nephrology. The group outlines foundational principles to guide AI development: prioritizing patient benefit, ensuring clinician oversight, and advancing innovation in high-burden disease areas. Its set of foundational assumptions are grounded in the physician always being in the loop and an overarching goal to benefit patients with kidney diseases. This review provides an overview of the clinical uses of AI in nephrology and offers practical guidance for nephrologists seeking to incorporate AI into CKD and AKI management, dialysis, and transplantation care. It also highlights key challenges-such as data quality, equity, transparency, and clinical integration-that must be addressed to ensure the responsible and effective implementation of AI in kidney care.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.