Design of an Intelligent Chatbot for Assisting Medical Diagnosis Within a Holistic Healthcare Approach in Burkina...

In low-resource healthcare environments such as Burkina Faso, limited access to medical professionals, combined with sociocultural barriers, creates delays in early diagnosis and care-seeking behavior. This paper presents the design, implementation, and evaluation of a culturally-aware intelligent medical chatbot that supports first-line diagnostic guidance within a holistic healthcare framework integrating physical, psychological, and contextual dimensions. Unlike conventional chatbots relying solely on general-purpose NLP models, the proposed system introduces a hybrid architecture combining rule-based reasoning and a domain-specific natural language processing engine (API G3N35I5) enriched with locally grounded symptom expressions. The system is designed to interpret both biomedical symptoms and culturally encoded expressions of discomfort. A pilot evaluation involving 25 participants demonstrates an average response time of 1.1 seconds, symptom interpretation accuracy of 87%, and user satisfaction of 92%, indicating that culturally-adapted AI systems can significantly improve early health orientation in low-resource contexts.