HerHealthGPT grounds reproductive-health answers in retrieval and clarification
Original title: HerHealthGPT: An Evidence-Grounded Conversational AI System for Women's Reproductive Health Copy title
On 540 messages from 90 cases, retrieval lifted concern-category accuracy from 54.1% to 59.3%.
The paper introduces HerHealthGPT, a conversational system for women's reproductive health that uses retrieval-augmented generation, context-aware clarification, and source validation. It asks follow-up questions instead of answering incomplete queries immediately and grounds replies in retrieved evidence. A prototype questionnaire with 30 adults found most preferred the retrieval-enabled setup. The authors say further clinical validation is still needed.
Unconfirmed: Clinical usefulness is not established beyond a 30-adult prototype questionnaire.
- Clarification router reached 85.6% recall and 86.2% specificity on constructed targets.
- 83.3% of 30 adults preferred the retrieval-enabled configuration.
- 25 of 30 participants considered its follow-up questions relevant.
- Authors say further clinical validation remains necessary.
From the original article
1 min read at arxiv.org"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.
By Hassan Saeed Hassan Albattra · arxiv.org
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