ReviewJournal of personalized medicine2023
Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT.
Review in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Generative AI in psychiatric education: balancing risks and benefits for responsible integration.Frontiers in psychiatry · 2026Article
- Large language models in nephrology: applications and challenges in chronic kidney disease management.Renal failure · 2025Review
- Performance evaluation of large language models in pediatric nephrology clinical decision support: a comprehensive assessment.Pediatric nephrology (Berlin, Germany) · 2025Article
- Article
- GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions.Scientific reports · 2025Article
- Article
- How to incorporate generative artificial intelligence in nephrology fellowship education.Journal of nephrology · 2024Article
- Identification of kidney-related medications using AI from self-captured pill images.Renal failure · 2024Article
- How to incorporate generative artificial intelligence in nephrology fellowship education.Journal of nephrology · 2024Article
- Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.Journal of surgical oncology · 2024Article
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Enhancing clinical decision-making: Optimizing ChatGPT's performance in hypertension care.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
- Joint Expedition: Exploring the Intersection of Digital Health and AI in Precision Medicine with Team Integration.Journal of personalized medicine · 2024Article
- Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.Medicina (Kaunas, Lithuania) · 2024Review
- Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.Journal of personalized medicine · 2024Article
- Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention.Journal of personalized medicine · 2024Article
- Chain of Thought Utilization in Large Language Models and Application in Nephrology.Medicina (Kaunas, Lithuania) · 2024Review
- Exploring the proficiency of ChatGPT-4: An evaluation of its performance in the Taiwan advanced medical licensing examination.Digital healthArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT's performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT's potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care.
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.