Safety-focused development and evaluation of an LLM sexual well-being chatbot for women: A methods-focused feasibility study

Journal article

Background: Large language models (LLMs) offer opportunities for sexual health education, but integrating them into digital products presents clinical challenges and risks, particularly regarding clinical safety and monitoring at scale. This study designed and evaluated a safety-focused framework for an LLM-based sexual well-being chatbot in a mobile application. Methods: We conducted a methods-focused feasibility study comprising a multi-stage development and evaluation of the chatbot. We used an interdisciplinary, medically led three-phase development process, including a five-stage evaluation framework combining synthetic test cases, clinician-led vulnerability testing and controlled release to real users to assess clinical accuracy and safety. Results: The chatbot met predefined precision and recall thresholds in synthetic testing. Clinically inaccurate responses remained below 2% across clinician review stages, with no high-severity unsafe responses. In a controlled release, 5195 real user interactions were reviewed. Clinically inaccurate responses occurred in 0.90% (47/5195) of dialogues, with unsafe responses within severity thresholds. Conclusion: This study demonstrates the feasibility of a structured framework for developing and evaluating LLM-based sexual health chatbots with clinical safety oversight. This approach helps to address gaps in safety reporting and could be adapted for other sensitive clinical domains.

Authors: McGee, AE; Parsons, G; Zhaunova, L; Paul, A; Kazlou, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SAGE Publications
• Journal: Digital Health
• Volume: 12
• Article number: 20552076261435861
• Publication date: 2026-04-15
• Acceptance date: 2026-03-09
• DOI: 10.1177/20552076261435861
• EISSN: 2055-2076
• ISSN: 2055-2076
• Language: English
• Keywords: chatbots; Large language models; clinical safety; digital health; risk assessment; human-in-the-loop AI