Desarrollo de un asistente virtual conversacional para la capacitación en auto muestreo y detección del VPH

Abstract

Cervical cancer (CC), mainly caused by Human Papillomavirus (HPV), is one of the leading causes of death among women, particularly in rural areas with limited access to healthcare. HPV self-sampling is an effective and accepted alternative, but its adoption faces barriers such as lack of information and guidance. This thesis presents the development of a conversational Virtual Assistant (VA) integrated into an Android mobile app to educate women aged 30 to 65 about self-sampling and sexual and reproductive health. The VA combines a chatbot built with the Rasa framework and a large language model (LLM) to handle queries outside the trained dataset. A dataset of 212 validated question-answer pairs was created, incorporating colloquial language from the target population. Initially, a local LLM was implemented for offline use, but due to performance issues and app size, it was replaced with a rule-based chatbot. Laboratory and final usability tests with women from Baños parish showed high acceptance, with users highlighting the system's clarity, usefulness, and ease of use. The System Usability Scale (SUS) questionnaire yielded an average score of 90.6/100, indicating excellent usability. Overall, the developed system proves to be a viable solution for improving access to reliable medical information in rural settings.