ReviewBMC nephrology2024
Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.
Review in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for Acute Kidney Injury in Stroke Patients: A Systematic Review.Brain and behavior · 2026Pooled it
- The role and utility of artificial intelligence and machine learning for diagnostic prediction in general practice.The European journal of general practice · 2026Article
- A nine-gene diagnostic model for IgA nephropathy based on multi-cohort machine learning: integrating gene expression and immunohistochemical validation.Renal failure · 2026Article
- DCBM-Tri: a dual-channel bilinear mapping triplet model for early recognition of acute kidney injury in imbalanced cohorts.Scientific reports · 2026Article
- External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.Critical care explorations · 2026Article
- Artificial intelligence for predicting paediatric acute kidney injury: a systematic review and meta-analysis.Clinical kidney journal · 2026Article
- A Dynamic Online Nomogram for Predicting Postoperative Acute Kidney Injury After Sleeve Gastrectomy.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Calibration and prediction of results after failed injection in SPECT renal dynamic imaging.American journal of nuclear medicine and molecular imaging · 2026Article
- From haemodynamics to kidney risk: AI-based early prediction validated in general and burn ICU populations.European heart journal. Digital health · 2026Article
- Machine learning models for predicting renal injury in patients with gout.Renal failure · 2025Article
- Building and Prospectively Evaluating a Prediction Model to Forecast Urgent Dialysis Needs across Four Tertiary Hospitals.American journal of nephrology · 2025Article
- An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study.Journal of anesthesia, analgesia and critical care · 2025Article
- Predicting Acute Kidney Injury in Acute Rhabdomyolysis.Journal of clinical medicine · 2025Article
- Cystatin C-based equations: Enhancing accuracy in kidney function tests for type 2 diabetes.World journal of nephrology · 2025Article
- Acute kidney injury: pathogenesis and therapeutic interventions.Molecular biomedicine · 2025Review
- Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).The ultrasound journal · 2025Article
- Recent Advances in Urinometers: Enhancing Monitoring of Urine Output, pH, and Color: A Narrative Review.Journal of pharmacy & bioallied sciences · 2025Review
- Artificial Intelligence (AI) and the future of Iran's Primary Health Care (PHC) system.BMC primary care · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.
Indexed as
Identifiers
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.