Multilingual AI-Based Healthcare Chatbot using FastAPI: A Secure Framework for Translation, Health Monitoring, and Real-Time Alerts

The rapid digitalization of medical services has amplified the need for accessible, secure, and highly responsive automated healthcare assistants. This paper presents the design, development, and hypothetical evaluation of a multilingual AI-based healthcare chatbot orchestrated through a high-performance FastAPI backend. The primary problem addressed is the persistent language barrier in digital health applications, coupled with the lack of integrated, real-time health monitoring and alert systems in conventional chatbots. To overcome these limitations, our methodology utilizes a modular architecture encompassing natural language processing (NLP), real-time language translation, and secure persistent storage using SQLite. User inputs are dynamically translated, processed by an AI engine for medical intent recognition, and evaluated against predefined clinical thresholds to trigger automated alerts. Simulated benchmarking of the proposed system demonstrates that leveraging a lightweight, Python-based REST service facilitates low-latency inference while maintaining high throughput for concurrent users. The results indicate that this framework not only broadens accessibility for non-native speakers but also serves as a robust foundation for real-time clinical decision support. Ultimately, this research provides a scalable blueprint for deploying privacy-centric, multilingual healthcare chatbots in diverse demographic settings.