While Artificial Intelligence (AI) shows promise in healthcare applications, existing conversational systems often falter in complex and sensitive medical domains such as Sexual and Reproductive Health (SRH). These systems frequently struggle with hallucination and lack the specialized knowledge required, particularly for sensitive SRH topics. Furthermore, current AI approaches in healthcare tend to prioritize diagnostic capabilities over comprehensive patient care and education. Addressing these gaps, this work at the UNC School of Nursing introduces SARHAchat, a proof-of-concept Large Language Model (LLM)-based chatbot. SARHAchat is designed as a reliable, user-centered system integrating medical expertise with empathetic communication to enhance SRH care delivery. Our evaluation demonstrates SARHAchat's ability to provide accurate and contextually appropriate contraceptive counseling while maintaining a natural conversational flow. The demo is available at https://sarhachat.com/}{https://sarhachat.com/.

翻译：尽管人工智能在医疗健康应用中展现出潜力，但现有的对话系统在性与生殖健康这类复杂且敏感的医学领域往往表现不佳。这些系统经常出现幻觉问题，并缺乏必要的专业知识，尤其是在敏感的SRH话题上。此外，当前医疗健康领域的人工智能方法往往优先考虑诊断能力，而非全面的患者护理与教育。为弥补这些不足，北卡罗来纳大学护理学院的这项工作推出了SARHAchat，一个基于大语言模型的概念验证聊天机器人。SARHAchat被设计为一个可靠、以用户为中心的系统，它将医学专业知识与共情沟通相结合，旨在提升SRH护理服务的质量。我们的评估表明，SARHAchat能够提供准确且情境适宜的避孕咨询，同时保持自然的对话流程。演示版本可在 https://sarhachat.com/ 获取。