Evidence map›Paper›PMID 39949434›Full record

ArticleCureus2025

Usefulness and Ease of Use of an Artificial Intelligence Chatbot for Kidney Stone Formers.

Mulun Huang, Kymora Scotland

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

2 authors.

Mulun HuangDepartment of Urology, University of California Los Angeles, Los Angeles, USA.
Kymora ScotlandDepartment of Urology, University of California Los Angeles, Los Angeles, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Kidney stone formers need to navigate an overwhelming amount of information for acute stone management and stone recurrence prevention. Artificial intelligence (AI) chatbots may be uniquely equipped to support stone formers in this process. UroGPT™ (Dornier Medtech, Wessling, Germany) is an AI chatbot developed to answer kidney stone-related questions and help kidney stone formers manage their care. The aim of this study is to evaluate the perceived usefulness and ease of use of UroGPT™ among kidney stone formers. Methods A pre-survey and a post-survey were designed to assess participants' experiences with their sources of kidney stone-related information before using UroGPT™, and their experiences using UroGPT™, respectively. Results In all, 626 and 260 participants participated in the pre- and post-survey, respectively. Among post-survey participants, 190 (73.08%) strongly agreed that using UroGPT™ makes it easier for them to get the information they need about kidney stones, 183 (70.38%) strongly agreed that they are satisfied with UroGPT™ for improving their understanding of kidney stones, and 218 (83.85%) strongly agreed that they find UroGPT™ easy to use. UroGPT™ achieved a total Net Promoter Score (NPS) of +75.77. Conclusion This study found that UroGPT™ is generally easy to use and useful for kidney stone formers. A high NPS indicates the potential for UroGPT™ to be widely adopted in kidney stone former communities.

Indexed as

ai chatbothealth educationhealth technologykidney stonelarge language model

Identifiers

PMID39949434
PMCPMC11822055

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Registered trials

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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.