ReviewSeminars in dialysis2021
Artificial intelligence enabled applications in kidney disease.
Review in Seminars in dialysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled 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.
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
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling.International urology and nephrology · 2024Pooled it
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.BMC infectious diseases · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 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
- Comparison of AI-generated renal diets by different large language models: a guideline-based evaluation with expert input.BMC nephrology · 2026Article
- Health-economic evaluation of an AI-powered decision support system for anemia management in in-center hemodialysis patients.BMC nephrology · 2025Article
- Functional Status in Elderly Kidney Transplant Recipients: A Systematic Review Evaluating Physical Function, Frailty, and Cognitive Impairment as Predictors of Post-Transplant Outcomes.Diseases (Basel, Switzerland) · 2025Review
- Advancing Cardiovascular, Kidney, and Metabolic Medicine: A Narrative Review of Insights and Innovations for the Future.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Review
- Artificial Intelligence Applications in Decision-Making for Disease Management:Sultan Qaboos University medical journal · 2025Article
- A population based optimization of convolutional neural networks for chronic kidney disease prediction.Scientific reports · 2025Article
- Synthetic data as an investigative tool in hypertension and renal diseases research.World journal of methodology · 2025Article
- Revolutionizing renal biopsy: the emerging role of omics and artificial intelligence in nephrology.Frontiers in medicine · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Review
- Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.Scientific reports · 2024Article
- Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning.BMC nephrology · 2024Article
- Review
- Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.
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What OpenQuestion 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 OpenQuestion graph.