ReviewMedicina (Kaunas, Lithuania)2024
Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.
Review in Medicina (Kaunas, Lithuania), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
What it found
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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.
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Who cites it
66 citing papers in PubMed.
- Assessment of accuracy, quality, and readability of large language models in kidney transplantation practice.World journal of urology · 2026Article
- Education Research: Validation of an AI-Powered Educational Platform for Neuroanatomical Localization and Stroke Syndrome Recognition: A Multicenter Study.Neurology. Education · 2026Article
- Assessing Quality, Readability, and Transparency of Online and AI-Generated Information on Type 2 Diabetes Mellitus: Cross-Sectional Exploratory Study.JMIR diabetes · 2026Article
- Improving self-care behaviors in patients with chronic obstructive pulmonary disease using a knowledge-based intelligent self-care system: protocol for a randomized controlled trial.Journal of thoracic disease · 2026Article
- Hyper-RAG: combating LLM hallucinations using hypergraph-driven retrieval-augmented generation.Nature communications · 2026Article
- CuAgent provides a RAG-assisted intelligent framework to investigate cuproptosis.Scientific reports · 2026Article
- Article
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from structured coronary CTA reports.Radiologie (Heidelberg, Germany) · 2026Article
- Guidelines vs. generative AI in CKD patient education: the role of prompt engineering and expert blinded evaluation.BMC nephrology · 2026Article
- Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency.International dental journal · 2026Article
- Ethical Imperatives for Retrieval-Augmented Generation in Clinical Nursing: Viewpoint on Responsible AI Use.JMIR medical informatics · 2026Article
- GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept.JMIR diabetes · 2026Article
- Risk Management of Large Language Model-Based Exercise and Health Guidance: A China-Anchored, Comparatively Informed Six-Dimensional Trigger Matrix and Lifecycle Governance Framework for the Wellness-to-SaMD Continuum.Risk management and healthcare policy · 2026Review
- Preliminary evaluation of DeepSeek-R1 and GPT-5.3 in selected PET/CT clinical scenarios: patient preparation, report interpretation, and diagnostic reasoning.Frontiers in medicine · 2026Article
- An explainable graph retrieval augmented generation framework for personalized nutrition recommendation.Frontiers in artificial intelligence · 2026Article
- Performance of Large Language Models in Analyzing Common Hypertension Scenarios.Hypertension (Dallas, Tex. : 1979) · 2026Article
- TCM-DiffRAG: personalized syndrome differentiation reasoning method for traditional Chinese medicine based on knowledge graph and chain of thought.Frontiers in medicine · 2026Article
- Improving core facility service discovery with an AI assistant grounded in institutional web content.Journal of biomolecular techniques : JBT · 2026Article
- Medical QA dialogue datasets in RAG systems performance evaluation and ChatGPT optimization.Scientific reports · 2025Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations-outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.
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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.