SARHAchat: An LLM-Based Chatbot for Sexual and Reproductive Health Counseling

2025-10-17

AI Summary

Existing SRH (sexual and reproductive health) dialogue systems suffer from frequent hallucinations, insufficient domain expertise, and lack of empathy—often prioritizing diagnosis over patient education and holistic care. To address these limitations, we propose the first user-centered, contraception-focused SRH chatbot. Our approach innovatively integrates clinical guideline–enhanced knowledge grounding, context-aware reasoning, and emotion-aware dialogue flow control to enable empathetic, medically accurate interactions. Outputs are rigorously constrained to adhere to evidence-based clinical standards. Comprehensive multidimensional evaluation demonstrates that the system delivers contextually appropriate, accurate, and reliable contraceptive recommendations while preserving natural, fluent conversational pacing. A functional demonstration version has been open-sourced and deployed.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Intelligent Robots: Human-Robot Interaction

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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/.

Problem

• Addressing hallucination in SRH conversational AI systems
• Integrating medical expertise with empathetic patient communication
• Providing accurate contraceptive counseling through natural dialogue

Innovation

• LLM-based chatbot for sexual health counseling
• Integrates medical expertise with empathetic communication
• Provides accurate contraceptive counseling naturally