This paper is in the following e-collection/theme issue:

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911) Peer-to-Peer Support and Online Communities (797) Nephrology/Renal Disorders (99) Artificial Intelligence (2129) Chatbots and Conversational Agents (740) Generative Language Models Including ChatGPT (708)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66365, first published .
Identifying Kidney Stone Risk Factors Through Patient Experiences With a Large Language Model: Text Analysis and Empirical Study

Identifying Kidney Stone Risk Factors Through Patient Experiences With a Large Language Model: Text Analysis and Empirical Study

Identifying Kidney Stone Risk Factors Through Patient Experiences With a Large Language Model: Text Analysis and Empirical Study

Authors of this article:

Chao Mao1 Author Orcid Image ;   Jiaxuan Li1 Author Orcid Image ;   Patrick Cheong-Iao Pang1 Author Orcid Image ;   Quanjing Zhu2 Author Orcid Image ;   Rong Chen3 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Wei Z, Zou H, Pang P, Chao P, Ng B. WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition. Applied Sciences 2025;15(12):6632 View
  2. Jin Z, Shuai Y, Li Y, Chen M, Liu Y, Lei W, Fan X. A Vision-Language–Guided Multimodal Fusion Network for Glottic Carcinoma Early Diagnosis: Model Development and Validation Study. JMIR Medical Informatics 2025;13:e74902 View