Privacy-by-Design Framework for Large Language Model Chatbots in Urology

Abstract

This review presents a privacy-by-design-based technical and governance framework for the safe clinical deployment of large language model (LLM) chatbots in urology. Given the high sensitivity of urological data involving urinary, sexual, and reproductive health, the proposed approach integrates on-site algorithmic deidentification, federated learning with differential privacy and secure aggregation, and secure retrieval-augmented generation with source citation and audit logging. Collectively, these components establish a federated, explainable, and auditable pipeline that preserves data sovereignty while improving clinical reliability and regulatory compliance. Urology thus serves as a critical test bed for validating the safety, governance, and accountability standards required for broader adoption of LLM-based medical chatbots across clinical domains.

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