SCAN: A HealthCare Personalized ChatBot with Federated Learning Based GPT

Abstract

Our paper introduces a groundbreaking approach to healthcare information retrieval and engagement through a personalized chatbot system empowered by Federated Learning Based GPT. The system is designed to seamlessly aggregate and curate diverse healthcare data sources, including research papers, multimedia resources, and news articles. Leveraging Federated Learning techniques, the GPT model is trained on decentralized data sources to ensure privacy and security while providing personalized insights and recommendations. Users interact with the chatbot through an intuitive interface, accessing tailored information and real-time updates on medical research and news. The system's innovative architecture enables efficient processing of input files, parsing and enriching text data with metadata, and generating relevant questions and answers using advanced language models. By facilitating interactive access to a wealth of healthcare information, this personalized chatbot system rep-resents a significant advancement in healthcare communication and knowledge dissemination.

Ähnliche Arbeiten