Symptotrackai: Hybrid RAG Chatbot for Symptom Monitoring
Year: 2026
Type: article
Abstract: In recent years, intelligent healthcare solutions have gained momentum in facilitating early diagnosis and proactive health management. SYMPTOTRACKAI presents a novel approach to symptom monitoring through a Hybrid Retrieval-Augmented Generation (RAG) chatbot, which combines the strengths of large language models with domain-specific knowledge retrieval systems. Designed to serve as a virtual health assistant, SYMPTOTRACKAI enables users to input symptoms via a conversational interface and receive accurate, context-aware responses that are grounded in trusted medical literature. The system leverages Natural Language Processing (NLP) for symptom recognition and employs a hybrid RAG architecture to fetch relevant clinical information, enhancing the chatbot's ability to provide evidence-backed recommendations. Additionally, the model incorporates a symptom severity classifier to assess the urgency of the reported symptoms and suggests appropriate actions, such as home care, medical consultation, or emergency attention. With support for multiple languages and a strong focus on user privacy and ethical AI use, SYMPTOTRACKAI offers a scalable and accessible solution for everyday health tracking. The integration of retrieval-based models with generative AI ensures both precision and fluency in communication, positioning SYMPTOTRACKAI as a powerful tool for preliminary health assessment and awareness in modern telehealth ecosystems.
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225623132
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