arXiv AI
A Safety-First Gateway Architecture for Trusted Public Health Resource Navigation
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arXiv AI
Jul 16
Designing Safety-Constrained LLM Systems for Public Health Information Access
arXiv:2607. 13038v1 Announce Type: cross Abstract: We present the design and implementation of a safety constrained large language model (LLM) system for public health information access, focusing on maternal and child health (MCH) resource navigation.
arXiv AI
Jul 17
JEEVHITAA -- An HCAI Ecosystem to Support Collective Care
arXiv:2512. 06364v4 Announce Type: replace-cross Abstract: Current mobile health platforms are predominantly individual-centric and lack the support for coordinated, auditable multi-actor workflows.
arXiv AI
Jul 29
PATHFinder Agent for Tailored Prenatal Care
arXiv:2607. 24768v1 Announce Type: new Abstract: Prenatal care is an important preventive service designed to improve outcomes for pregnant individuals.
arXiv AI
Sep 15
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
ClinAgent is a conversational system that uses a ReAct-based LLM agent to retrieve and synthesize clinical trial information from multiple sources such as ClinicalTrials.gov, PubMed, and a local dataset. The agent iteratively reasons over user queries, selects appropriate tools, and refines its actions to provide grounded, up-to-date responses in natural language across multi-turn interactions. Evaluation across three phases shows that DeepSeek (thinking mode) excels in planning quality while Gemini 3.0 Flash delivers the highest overall performance and expert ratings, demonstrating the promise of agentic AI for improving clinical trial data access.
arXiv AI
Sep 25
CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
CRISS is a retrieval‑augmented generation chatbot designed to aid cancer registrars by providing rapid, citation‑supported answers to complex coding and staging questions. It uses a domain‑specific knowledge base of national registry standards, indexed as dense embeddings, to retrieve relevant passages that ground responses generated by a large language model. In evaluations, RAG configurations consistently outperformed non‑RAG baselines across easy, medium, and hard questions, achieving higher grounding and semantic similarity scores while maintaining human oversight for final decisions.
arXiv Computation and Language
Sep 2
Toward Workflow-Aware Benchmarking for Healthcare NLP Agents
arXiv:2609.00296v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly proposed for healthcare tasks such as clinical documentation, evidence retrieval, patient messaging,...