The investigation of symptom to disease chatbot based on LLAMA-2

In the swiftly advancing realm of Artificial Intelligence (AI), there has been a remarkable evolution in Natural Language Processing (NLP). Language models like ChatGPT have ushered in a new era of human-computer interaction, bestowing upon people an unparalleled capacity to comprehend and generate text that mimics human language. When these models are fine-tuned for specific domains, such as medicine, they become endowed with domain-specific knowledge, bridging the chasm between language comprehension and specialized expertise. However, a noteworthy research gap exists when it comes to harnessing AI-powered NLP for medical diagnosis and symptom analysis, particularly in the development of a Symptom to Disease Chatbot. This innovative approach aims to provide accurate and accessible healthcare information, enhancing the initial steps of medical consultation. This research proposes and develops a pioneering Symptom to Disease Chatbot, powered by the sophisticated Llama 2 13b pre-trained language model. By integrating extensive medical knowledge and AI capabilities, this chatbot seeks to empower individuals to make informed decisions about their health, potentially transforming personal health management. The research includes data preparation involving curated medical datasets, transforming them into a conversational format suitable for training. The base model, Llama 2, is fine-tuned for this medical context. While preliminary results are promising, further exploration with larger datasets is essential to enhance performance. Additionally, an open-source Gradio interface enhances user interaction. This research addresses the critical need for accessible healthcare information and demonstrates the potential of AI-powered language models in the healthcare sector.