ReviewKidney medicine2024
Precision Dialysis: Leveraging Big Data and Artificial Intelligence.
Review in Kidney medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Dynamic potassium variability (K-CV) as an independent predictor of mortality in maintenance hemodialysis.Renal failure · 2026Article
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.Clinical and experimental nephrology · 2026Review
- Artificial Intelligence in Dialysis Therapies:Saudi medical journal · 2026Review
- Updates in the management of intradialytic hypotension: Emerging strategies and innovations.World journal of nephrology · 2025Review
- Safety and Efficacy of the First Mobile Intelligent Hemodialysis Platform in Mainland China: Protocol for a Single-Center Prospective Exploratory Study in Maintenance Hemodialysis Patients.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The long-term mortality of patients with kidney failure remains unacceptably high. There are a multitude of reasons for the unfavorable status quo of dialysis care, such as the inadequate and suboptimal pattern of uremic toxin removal resulting in a metabolic and hemodynamic "roller coaster" induced by thrice-weekly in-center hemodialysis. Innovation in dialysis delivery systems is needed to build an adaptive and self-improving process to change the status quo of dialysis care with the aim of transforming it from being reactive to being proactive. The introduction of more physiologic and smart dialysis systems using artificial intelligence (AI) incorporating real-time data into the process of dialysis delivery is a realistic target. This would enable machine learning from both individual and collective patient treatment data. This has the potential to shift the paradigm from the practice of population-driven, evidence-based data to precision medicine. In this review, we describe the different components of an AI system, discuss the studied applications of AI in the field of dialysis, and outline parameters that can be used for future smart, adaptive dialysis delivery systems. The desired output is precision dialysis; a self-improving process that has the ability to prognosticate and develop instant and individualized predictive models.
Indexed as
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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.