Evidence map›Paper›PMID 39470812›Full record

ArticleWorld journal of urology2024

Battle of the bots: a comparative analysis of ChatGPT and bing AI for kidney stone-related questions.

Amber K McMahon, Russell S Terry, Willian E Ito, Wilson R Molina, Bristol B Whiles

Abstract readComparative Study
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Amber K McMahonDepartment of Urology, University of Kansas Medical Center, Kansas City, KS, USA.ORCID http://orcid.org/0000-0003-0707-8671
Russell S TerryDepartment of Urology, University of Florida College of Medicine, Gainesville, FL, USA.
Willian E ItoDepartment of Urology, University of Kansas Medical Center, Kansas City, KS, USA.
Wilson R MolinaDepartment of Urology, University of Kansas Medical Center, Kansas City, KS, USA.
Bristol B WhilesDepartment of Urology, University of Kansas Medical Center, Kansas City, KS, USA. bwhiles@kumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Kidney CalculiGuideline AdherenceHumansPractice Guidelines as TopicSurveys and QuestionnairesUrologyArtificial intelligenceBing AIChatbotsChatGPTDISCERNEndourologyKidney stonesNephrolithiasis

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.