Developing a Virtual Diagnosis and Health Assistant Chatbot Leveraging LLaMA3

This paper presents the development of a Virtual Diagnosis and Health Assistant Chatbot using the LLaMA3 language model. The chatbot is designed to provide accurate health information and preliminary diagnoses. It leverages a fine-tuned LLaMA3 model, enhanced with the MedPalm dataset, to handle medical queries effectively. Fine-tuning techniques ensure that the responses generated are both relevant and reliable. The system includes API endpoints that connect to a user-friendly interface built with HTML and JavaScript. This interface allows users to interact with the chatbot by submitting text queries or uploading images for analysis. Comprehensive API documentation and testing were carried out using Swagger. The chatbot achieves an accuracy of 88% in medical diagnosis tasks, offering significant improvements over existing models. Future work will focus on expanding the chatbot's capabilities, including multilingual support and audio input features. This implementation demonstrates the potential of advanced language models in improving healthcare access and efficiency.