A Multilingual AI-Driven Conversational System for Cervical Cancer Awareness and Risk Assessment: Development and Evaluation Study

A Multilingual AI-Driven Conversational System for Cervical Cancer Awareness and Risk Assessment: Development and Evaluation Study. BACKGROUND: Cervical cancer is the fourth most common cancer among women globally, with an estimated 660,000 new cases and 350,000 deaths in 2022. It imposes a disproportionate burden on underserved populations in low- and middle-income countries, where language barriers, cultural stigma, and limited access to health care hinder awareness and uptake of preventive measures. OBJECTIVE: This study aimed to design, develop, and evaluate a multilingual AI-driven dialogue system for cervical cancer awareness, personalized risk assessment, and health care facility navigation, targeting underserved populations through a culturally adaptive and privacy-preserving digital health platform. METHODS: A design science research framework guided the development of a 3-tier, cross-platform system comprising (1) a transparent, rule-based risk-stratification engine whose 30-item questionnaire mirrors the feature schema of the University of California, Irvine (UCI) Cervical Cancer Risk Factors dataset and whose weights encode established clinical risk factors drawn from World Health Organization, US Centers for Disease Control and Prevention (CDC), and American Cancer Society guidance; (2) a retrieval-augmented generation (RAG) chatbot powered by GPT-4o mini; and (3) a geospatial health care facility recommendation module. The system was implemented in TypeScript (NestJS) and Flutter (Google LLC) and deployed across Android, iOS, and web platforms with multilingual support for English, Spanish, Yoruba, and Swahili, of which English, Spanish, and Yoruba were included in the multilingual evaluation reported here. The risk engine was evaluated through clinical face-validity review of its factor weights; chatbot quality was assessed using retrieval precision, hallucination rate, large language model (LLM)-as-a-judge quality scoring, adversarial safety testing,…