HerHealthGPT improves reproductive health chats with smarter questions
HerHealthGPT: An Evidence-Grounded Conversational AI System for Women's Reproductive Health Copy title
Computation and Language
Summary
People often ask questions about women’s reproductive health that are unclear or incomplete. The authors created HerHealthGPT, an AI chatbot that doesn’t rush to answer but instead asks follow-up questions to better understand the user’s concerns. It finds trustworthy information and uses it to give clearer, more accurate advice about topics like periods. The system was tested with sample messages and real users, who mostly preferred its careful, evidence-based approach. While promising, the system needs more clinical testing before full use.
What this means in practice
- •For health chatbot developers: Create AI assistants that ask clarifying questions and provide evidence-backed answers for women’s reproductive health topics.
- •For telehealth service providers: Integrate a conversational AI interface that improves patient interaction by avoiding premature advice and confirming user context in reproductive health inquiries.
Authors
Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi, Hana Essam Sayed Ahmed Amrya, Mariam Mousa
Abstract
"Why is my period late?" Sometimes, the most useful response begins with another question. Yet conversational AI systems may respond to incomplete health queries without seeking essential context or grounding their guidance in verifiable evidence. This paper introduces HerHealthGPT, an evidence-grounded conversational AI system designed to address these challenges in women's reproductive health. HerHealthGPT integrates retrieval-augmented generation (RAG), context-aware clarification, and source validation within an interactive web interface. Rather than responding immediately to incomplete queries, the system maintains conversational context, asks targeted follow-up questions, checks for premature conclusions, and grounds its responses in retrieved evidence. We evaluate the AI components using 540 English messages derived from 90 reproductive-health cases and conduct a prototype questionnaire study with 30 adults. Evidence retrieval improves concern-category accuracy from 54.1% to 59.3% compared with fine-tuning alone. The clarification router achieves 85.6% recall and 86.2% specificity on constructed targets. In the user study, 83.3% of participants preferred the retrieval-enabled configuration, while 25 of 30 considered its follow-up questions relevant. These findings suggest that integrating evidence retrieval with interactive clarification offers a promising approach to more transparent, context-aware reproductive-health information systems, while further clinical validation remains necessary.