ArticleClinical kidney journal2023
Artificial intelligence: a new field of knowledge for nephrologists?
Article in Clinical kidney journal, 2023. 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.
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- The evolving field of nephrology: what comes next? A report from the European Renal Association Scientific Advisory Board.Clinical kidney journal · 2026Review
- 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
- Cost-Effectiveness Analysis of Artificial Intelligence-Driven Risk Stratification in Patients With Diabetic Kidney Disease in the US Veterans Population.Kidney medicine · 2026Article
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Large language models in nephrology: applications and challenges in chronic kidney disease management.Renal failure · 2025Review
- Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Statistical consideration in nephrology research.Kidney research and clinical practice · 2025Article
- 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
- When the whole is greater than the sum of its parts: why machine learning and conventional statistics are complementary for predicting future health outcomes.Clinical kidney journal · 2025Review
- Kidney Fibrosis In Vitro and In Vivo Models: Path Toward Physiologically Relevant Humanized Models.Advanced healthcare materials · 2025Review
- A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.Scientific reports · 2025Article
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Artificial Intelligence in Clinical Trials: A Comparative Study With Nephrologists in Prescreening.Kidney international reports · 2025Article
- International Nephrology Masterclass in Chronic Kidney Disease: Rationale, Summary, and Future Perspectives.Life (Basel, Switzerland) · 2024Review
- Advances in Human-Centered Care to Address Contemporary Unmet Needs in Chronic Dialysis.International journal of nephrology and renovascular disease · 2024Review
- The leading global health challenges in the artificial intelligence era.Frontiers in public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.
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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.