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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Privacy-by-Design Framework for Large Language Model Chatbots in Urology.

Eun Joung Kim1, JungYoon Kim2

  • 1Department of Game Contents, Kyungil University, Gyeongsan, Korea.

International Neurourology Journal
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

This review proposes a privacy-by-design framework for safe clinical use of large language model (LLM) chatbots in urology. It ensures data protection and regulatory compliance for sensitive health information.

Keywords:
Large language modelMedical chatbotPrivacy-by-design frameworkUrology

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Urology

Background:

  • Large language models (LLMs) offer potential in healthcare but raise privacy concerns due to sensitive data.
  • Urological data, including urinary, sexual, and reproductive health information, is particularly sensitive.
  • Existing frameworks may not adequately address the unique privacy and governance needs for LLMs in urology.

Purpose of the Study:

  • To present a privacy-by-design technical and governance framework for safe clinical deployment of LLM chatbots in urology.
  • To ensure the protection of sensitive urological data while enabling reliable clinical applications.
  • To establish standards for safety, governance, and accountability for LLM adoption in medicine.

Main Methods:

  • Integration of on-site algorithmic deidentification for sensitive data.
  • Implementation of federated learning with differential privacy and secure aggregation.
  • Utilization of secure retrieval-augmented generation with source citation and audit logging.

Main Results:

  • A federated, explainable, and auditable pipeline is established.
  • Data sovereignty is preserved, enhancing patient trust and data security.
  • Clinical reliability and regulatory compliance are improved.

Conclusions:

  • The proposed framework enables safe and responsible deployment of LLM chatbots in urology.
  • Urology serves as a crucial test case for validating LLM safety and governance standards.
  • This approach facilitates broader adoption of LLM-based medical chatbots across diverse clinical domains.