ReviewPediatric nephrology (Berlin, Germany)2026
A brief review of some artificial intelligence methods in nephrology.
Review in Pediatric nephrology (Berlin, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.Renal failure · 2026Article
- Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026Review
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This brief, focused review considers two of the more commonly used artificial intelligence (AI) methods encountered in nephrology publications: machine vision based on convolutional neural networks (CNNs) and chatbots, such as ChatGPT, based on large language models. It is intended to offer a mostly non-technical, intuitive understanding of these methods, including their uses and limitations. CNNs have been used for some time to segment and classify important features of digitized kidney biopsy images. In addition to the identification of pathologic primitives, CNN approaches may be used to predict so-called sub-visual features of biopsies, such as kidney survival rates. Large language models are newer players in the medical AI field. Although seemingly easy to use as natural language tools, most currently available chatbots have been characterized by inconsistent performance, hallucinations, and even a higher CO
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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.