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  1. Home
  3. Evaluation Of Chatgpt For Patient Counseling In Kidney Stone Clinic: A Prospective Study.
  1. Home
  3. Evaluation Of Chatgpt For Patient Counseling In Kidney Stone Clinic: A Prospective Study.

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Evaluation of ChatGPT for Patient Counseling in Kidney Stone Clinic: A Prospective Study.

Mohamed Javid1, Mahendra Bhandari2, P Parameshwari3

  • 1Department of Urology, Chengalpattu Medical College, Chengalpattu, Tamil Nadu, India.

Journal of Endourology
|February 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models (LLMs) show potential in urology clinics. While ChatGPT provided higher-rated responses in accuracy and empathy, it is not yet ready for direct patient use.

Keywords:
ChatGPTlarge language modelspatient queriesrenal stone disease

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Area of Science:

  • Urology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Large language models (LLMs) offer potential to enhance clinical workflows and patient care efficiency.
  • The application of LLMs in patient counseling within specialized clinics requires thorough evaluation.
  • Urology stone clinics face challenges in providing timely and comprehensive patient information.

Purpose of the Study:

  • To prospectively evaluate ChatGPT's performance as a patient counseling tool in a urology stone clinic.
  • To compare ChatGPT-generated responses with those provided by experienced urologists.
  • To assess the accuracy, relevance, empathy, completeness, and practicality of LLM-generated responses.

Main Methods:

  • Collected 61 patient questions from 12 kidney stone patients.
  • Prompted questions to ChatGPT and a panel of urologists (Level 1).
  • Expert urologists (Level 2) blindly evaluated responses using a Likert scale and Cohen's kappa for interrater reliability.

Main Results:

  • ChatGPT responses were rated significantly higher in accuracy, empathy, completeness, and practicality (p < 0.001).
  • Relevance scores showed no significant difference between ChatGPT and urologists (p = 0.051).
  • Significant interrater agreement was found only in the empathy domain (k = 0.163).

Conclusions:

  • ChatGPT can supplement urologist responses, adding value to patient information in busy clinics.
  • LLMs like ChatGPT are not yet suitable as a sole source of authentic patient information.
  • Recommend using LLMs to develop comprehensive FAQ banks for future patient counseling chatbots.