ArticleInternational neurourology journal2025
Privacy-by-Design Framework for Large Language Model Chatbots in Urology.
Article in International neurourology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.