A Safety-First Gateway Architecture for Trusted Public Health Resource Navigation

Abstract:Conversational AI can improve access to public health information, but public-facing healthcare applications require safeguards against inappropriate medical guidance and unsupported generation. We present a Safety-First Science Gateway for maternal and child health (MCH) resource navigation that combines large language models (LLMs) and retrieval-augmented generation (RAG) with a multi-layer safety architecture. The gateway integrates emergency handling, domain/scope screening, source attribution, anonymous session management, and operational audit logging while restricting retrieval to curated institutional resources. We describe the gateway architecture, prototype implementation, and functional verification of selected workflows. The current system provides resource provenance and safety-bounded navigation; it does not constitute a clinical decision-support system or automated claim-by-claim verification of generated health information. This work provides a reusable architectural framework for conversational navigation of curated public-health resources.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI)

arXiv:2607.13038 [cs.CY]

(or arXiv:2607.13038v2 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2607.13038 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.