ארכיטקטורת גשר בטוחה לניווט במשאבי בריאות ציבוריים

תקציר מקורי באנגליתarXiv:2607.13038v2 Announce Type: replace-cross 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.