ArticleJournal of personalized medicine2024
Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention.
Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Assessing large language models as assistive tools in selecting first trial lens parameters for orthokeratology.Frontiers in medicine · 2026Article
- External Validation and Comparative Performance of the T.O.HO. and S.T.O.N.E. Scoring Systems for Predicting Stone-Free Outcomes Following Flexible Ureteroscopy: Toward Personalized Preoperative Counseling.Journal of personalized medicine · 2025Article
- Clinical applications and limitations of large language models in nephrology: a systematic review.Clinical kidney journal · 2025Article
- A cross-language analysis of urolithiasis patient online materials: Assessment across 24 European languages.Central European journal of urology · 2025Article
- Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971-2024).Medical journal of the Islamic Republic of Iran · 2025Article
- Identification of kidney-related medications using AI from self-captured pill images.Renal failure · 2024Article
- Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome.European journal of clinical nutrition · 2024Article
- Assessing the Accuracy of Artificial Intelligence Models in Scoliosis Classification and Suggested Therapeutic Approaches.Journal of clinical medicine · 2024Article
- Global Trends in Kidney Stone Awareness: A Time Series Analysis from 2004-2023.Clinics and practice · 2024Article
- Enhancing clinical decision-making: Optimizing ChatGPT's performance in hypertension care.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
- Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.Medicina (Kaunas, Lithuania) · 2024Review
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Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (
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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.