ReviewNature reviews. Nephrology2022
Artificial intelligence-enabled decision support in nephrology.
Review in Nature reviews. Nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed, 88 citations in OpenAlex.
- The rate of acute kidney injury (AKI) alert detection by the attending physicians was associated with the prognosis of patients with AKI.Frontiers in public health · 2022Trial
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.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
- Review
- Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Shaping the Future of Men's Health: How AI Could Be a Transformative Tool for Better Patient Outcomes and Provider Efficiency.Journal of medical Internet research · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Association between oxidative balance score, genetic susceptibility and nephrolithiasis: a cohort study based on the UK Biobank.European journal of nutrition · 2026Article
- Impact of Query Language on the Structure and Guideline Alignment of AI-Generated Rehabilitation Programs in Chronic Kidney Disease.Journal of clinical medicine · 2026Article
- A Blind Spot in the Algorithm: Assessing Bias in Artificial Intelligence-Generated Images of Plastic Surgery Patients.Plastic and reconstructive surgery. Global open · 2026Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 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
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- How do dialysis nurses and AI reason clinically? A scenario-based comparative study.BMC nursing · 2026Article
- Contraceptive Use and Reproductive Health in Women With CKD: A Qualitative Study of Nephrologists in the United States.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine.Frontiers in genetics · 2026Review
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Evaluating the potential of ChatGPT as an educational decision-support tool for hemodialysis decision-making in nephrology training.Frontiers in medicine · 2026Article
- Extreme phenotype-derived machine learning reveals susceptibility and resilience signatures for severe diabetic retinopathy.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.
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.