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Related Experiment Video

Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Chatbots' performance in premature ejaculation questions: a comparative analysis of reliability, readability, and

S Gonultas1, S Kardas2, M Gelmis2

  • 1Gaziosmanpasa Training and Research Hospital, Department of Urology, Istanbul, Turkey. dr.serkangonultas@hotmail.com.

International Journal of Impotence Research
|September 24, 2025
PubMed
Summary

AI chatbots offer reliable information on premature ejaculation but struggle with readability and source verification. Improvements are needed for better patient education and AI integration in healthcare.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Digital Health and Medical Information Dissemination
  • Sexual Health and AI Applications

Background:

  • Artificial intelligence (AI) chatbots are increasingly used for health information.
  • Premature ejaculation (PE) is a common sexual health concern.
  • Evaluating AI's role in providing accurate and accessible PE information is crucial.

Purpose of the Study:

  • To assess the reliability, readability, and understandability of AI chatbot responses concerning premature ejaculation (PE).
  • To evaluate the potential contributions, risks, and limitations of AI in delivering sexual health information.
  • To compare the performance of multiple AI chatbots (Copilot, Gemini, ChatGPT4o, ChatGPT4oPlus, DeepSeek-R1) on PE-related queries.

Main Methods:

  • Fifteen frequently asked questions about PE, identified via Google Trends, were posed to five AI chatbots.
  • Reliability was measured using the Global Quality Scale (GQS).
  • Readability was assessed using Flesch Kincaid Reading Ease (FKRE), Flesch Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), and Simple Measure of Gobbledygook (SMOG).
  • Understandability was evaluated using the Patient Educational Materials Assessment Tool for Printable Materials (PEMAT-P).
  • Source citation consistency was also examined.

Main Results:

  • ChatGPT4o, ChatGPT4oPlus, and DeepSeek-R1 demonstrated higher reliability (GQS scores) and understandability (PEMAT-P scores) compared to Copilot and Gemini (p < 0.001).
  • All chatbots performed at an acceptable level (≥70%) for reliability and understandability.
  • Readability scores across all chatbots exceeded recommended levels for the target audience, indicating potential barriers to comprehension.
  • Instances of low reliability and unverified sources were noted, with no significant differences among the chatbots.

Conclusions:

  • AI chatbots provide generally reliable and informative responses regarding premature ejaculation (PE).
  • Significant limitations exist, particularly concerning the readability of responses and the verification of cited sources.
  • Further development is needed to optimize AI-generated health information for clarity and accuracy, especially for sensitive topics like PE.