ArticleWorld journal of urology2024
Battle of the bots: a comparative analysis of ChatGPT and bing AI for kidney stone-related questions.
Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Performance of Large Language Models in Answering Healthcare Delivery Questions: A Quantitative Cross-Sectional Study.Health science reports · 2026Article
- A comparative evaluation of the quality of responses provided by different large language model chatbots to frequently asked questions regarding nerve blocks.BMC anesthesiology · 2026Article
- Large language models in nephrology: applications and challenges in chronic kidney disease management.Renal failure · 2025Review
- Large language model chatbots for patient education in kidney stones: a scoping review.World journal of urology · 2025Article
- Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease.Journal of clinical medicine · 2025Review
- What is the role of large language models in the management of urolithiasis?: a review.Urolithiasis · 2025Review
- Battle of the artificial intelligence: a comprehensive comparative analysis of DeepSeek and ChatGPT for urinary incontinence-related questions.Frontiers in public health · 2025Article
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
objectivesTo evaluate and compare the performance of ChatGPT™ (Open AI
methodsWe developed 20 kidney stone evaluation and treatment-related questions based on the AUA Surgical Management of Stones guideline. Questions were asked to ChatGPT and Bing AI chatbots. We compared their responses utilizing the brief DISCERN tool as well as response appropriateness.
resultsChatGPT significantly outperformed Bing AI for questions 1-3, which evaluate clarity, achievement, and relevance of responses (12.77 ± 1.71 vs. 10.17 ± 3.27; p < 0.01). In contrast, Bing AI always incorporated references, whereas ChatGPT never did. Consequently, the results for questions 4-6, which evaluated the quality of sources, were consistently favored Bing AI over ChatGPT (10.8 vs. 4.28; p < 0.01). Notably, neither chatbot offered guidance against guidelines for pre-operative testing. However, recommendations against guidelines were notable for specific scenarios: 30.5% for the treatment of adults with ureteral stones, 52.5% for adults with renal stones, and 20.5% for all patient treatment.
conclusionsChatGPT significantly outperformed Bing AI in terms of providing responses with clear aim, achieving such aim, and relevant and appropriate responses based on AUA surgical stone management guidelines. However, Bing AI provides references, allowing information quality assessment. Additional studies are needed to further evaluate these chatbots and their potential use by clinicians and patients for urologic healthcare-related questions.
Indexed as
Identifiers
39470812What 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.