Empowering Healthcare Data Systems with an Innovative Chatbot Application Utilizing Python and Advanced Generative AI Models

The exponential growth of healthcare data presents significant challenges in data accessibility, patient engagement, and operational efficiency. This paper introduces a novel chatbot application that leverages advanced generative AI models, specifically a fine-tuned LLaMA and an enhanced BioBERT, to empower healthcare data systems. The chatbot integrates innovative techniques in natural language processing (NLP), data preprocessing, and model fine-tuning to handle diverse tasks such as answering complex medical queries, analyzing various document types, recognizing medical conditions from prescriptions, and providing personalized health recommendations. The system's architecture is designed with scalability and real-world deployment in mind, utilizing asynchronous processing and optimized model serving strategies. Comprehensive experiments demonstrate significant improvements over baseline models in terms of accuracy, response time, and user satisfaction. Ethical considerations, including data privacy and compliance with regulations like HIPAA and GDPR, are thoroughly addressed to ensure responsible deployment in healthcare settings.