DawAI: AI Powered Medical Chatbot

Abstract

The increasing demand for accessible healthcare services, coupled with the shortage of medical professionals and geographical barriers, highlights the need for intelligent digital healthcare solutions. Traditional medical chatbots are largely limited to text-based interactions, lacking the ability to process multimodal inputs such as speech and medical images, thereby restricting their effectiveness in real-world scenarios. This paper presents DawAI, a multimodal AI-powered virtual medical assistant designed to simulate real-time doctor– patient interactions. The system integrates advanced technologies including speech-to-text conversion for interpreting spoken symptoms, image-based analysis for visual medical inputs, and large language models for generating contextaware medical responses. Additionally, a text-to-speech module enables the system to deliver responses in a natural, human-like voice, enhancing user accessibility and interaction. DawAI operates through a unified architecture that processes voice and image inputs, performs multimodal reasoning, and generates informative, empathetic responses within seconds. A structured dataset comprising symptom descriptions, severity levels, and precautionary measures supports the system’s reasoning capability, ensuring coherent and medically relevant outputs. Experimental evaluation demonstrates that the system provides consistent and context-sensitive responses while maintaining real-time performance. By addressing the limitations of existing healthcare chatbots, DawAI offers a scalable, accessible, and user-friendly solution for preliminary medical consultation, particularly benefiting users in remote and resource-constrained environments.