Authors: Ms. Gurpreet Kaur, Mayank Gupta, Kanak Sharma, Sarthak Goel
Abstract: The use of artificial intelligence (AI) in medicine has created medical Chat – bots that supports real -time patients, symptom assessment, early diagnosis and supportive patient training. However, traditional Chat – bot models based on static database or pre-influensing reactions have problems with chronic information, reference upheaval and the possibility of incorrect information. Recovery-sized generation (RAG) is a sophisticated AI model that supports the chat bot capacity by integrating a recovery system with generative AI, and ensures that reactions are relevant sounds and most infected with today's medical knowledge. This article emphasizes the main elements of the theoretical base and the real application of Raga-based medical chat bots that enable better accuracy, flexibility and user interactions. We discuss architecture, recycling process and response generation mechanisms that distinguish rag from traditional NLP – based chat-bots. In addition, we explain in detail about the significant strength of Rag, such as medical accuracy, real -time flexibility and adapted patient interaction. While the possibilities are very good, the implementation of carpet -based medical chat-bots is accompanied by computational overhead, data security and difficulties with regulatory requirements. We discuss these boundaries in adding possible solutions to make chat bot more reliable and effective. Case studies of real implementation also give us a picture of how effective they are and practically how they are used in modern health care. Finally, we identify future research directions by integrating RAG-based medical chat bot with new techniques such as IOT, Block chain and Multi-model AI to further change the digital health service. By addressing these main areas, this research tries to contribute to continuous progress of AI-driven medical chat bot, so that they can become an integral part of both health care professionals and patients.
DOI: http://doi.org/10.5281/zenodo.16608602
