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

arXiv: https://arxiv.org/abs/2607.13038 · HTML: https://mlsanity.com/paper/2607.13038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 1 of 1)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)

## Abstract (arXiv)

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.