ReviewKidney medicine2025
Artificial Intelligence in Nephrology: Clinical Applications and Challenges.
Review in Kidney medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Multi-Modal, Machine Learning-Driven Framework Integrating Multi-Omics for Personalized Chronic Kidney Disease Management.Journal of clinical medicine · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Next-generation therapeutics for diabetic kidney disease.Nature reviews. Nephrology · 2026Review
- Mineralocorticoid Receptor Antagonists in Dialysis.American journal of nephrology · 2026Review
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Dosage adjustments in renal impairment among medical ward patients: ChatGPT® and DeepSeek® models' effectiveness in assessing those adjustments.Exploratory research in clinical and social pharmacy · 2026Article
- Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.Diabetes, obesity & metabolism · 2026Article
- The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026Review
- KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules.Scientific reports · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.Renal failure · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Performance Comparison of a Neural Network and a Regression Linear Model for Predictive Maintenance in Dialysis Machine Components.Bioengineering (Basel, Switzerland) · 2025Article
- Future Designs of Clinical Trials in Nephrology: Integrating Methodological Innovation and Computational Power.Sensors (Basel, Switzerland) · 2025Review
- Explainable Machine Learning in the Prediction of Depression.Diagnostics (Basel, Switzerland) · 2025Article
- The future of nephrology in 2050.Future healthcare journal · 2025Article
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Between the algorithm and clinical reasoning.Jornal brasileiro de nefrologiaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is increasingly used in many medical specialties. However, nephrology has lagged in adopting and incorporating machine learning techniques. Nephrology is well positioned to capitalize on the benefits of AI. The abundance of structured clinical data, combined with the mathematical nature of this specialty, makes it an attractive option for AI applications. AI can also play a significant role in addressing health inequities, especially in organ transplantation. It has also been used to detect rare diseases such as Fabry disease early. This review article aims to increase awareness on the basic concepts in machine learning and discuss AI applications in nephrology. It also addresses the challenges in integrating AI into clinical practice and the need for creating an AI-competent nephrology workforce. Even though AI will not replace nephrologists, those who are able to incorporate AI into their practice effectively will undoubtedly provide better care to their patients. The integration of AI technology is no longer just an option but a necessity for staying ahead in the field of nephrology. Finally, AI can contribute as a force multiplier in transitioning to a value-based care model.
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.