AI-Driven Healthcare Chatbot on LINE for Maternal Complication Risk Assessment

Abstract

Maternal complications remain a critical global health concern, particularly in developing countries where access to timely diagnosis and healthcare consultation is limited. This study presents the design and development of an AI-driven healthcare chatbot integrated with the LINE platform, aimed at supporting early assessment of pregnancy-related risks and complications. The proposed system employs natural language processing (NLP) and rule-based decision logic to provide preliminary screening, personalized guidance, and health information for pregnant women and caregivers. Using the Build-Measure-Learn feedback loop and Design Thinking approach, the chatbot prototype was iteratively refined to ensure usability, accuracy, and accessibility. Evaluation results demonstrated that the system effectively reduces response time, enhances communication efficiency between users and healthcare providers, and increases maternal health awareness. The implementation suggests a scalable model for AI-assisted telehealth services that can be adapted to other specialized healthcare contexts.

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