Intelligent Medical Assistance Framework Powered by Large Language Models and Retrieval-Augmented Generation

Abstract

Artificial Intelligence (AI) has significantly transformed modern healthcare systems by improving healthcare accessibility, intelligent diagnosis support, and patient interaction. Traditional healthcare systems mainly depend on manual consultation processes and static healthcare platforms, which often fail to provide personalized and real-time medical assistance. Recent progress in generative language technologies, retrieval-based intelligence, conversational systems, and multimodal AI has supported the creation of advanced healthcare platforms that can interpret patient inputs and provide relevant medical assistance based on contextual understanding. This review paper presents a detailed study of AI-based intelligent healthcare assistance systems focusing on conversational healthcare technologies, retrieval-supported reasoning systems, and multimodal healthcare analysis frameworks. The paper reviews existing research related to machine learning-based disease prediction systems, healthcare chatbot technologies, medical image analysis systems, and Retrieval-Augmented Generation architectures. The study also discusses the role of semantic retrieval systems, vector databases, and intelligent reasoning approaches in improving the reliability and contextual understanding of healthcare responses. Furthermore, the paper analyses major challenges associated with AI-powered healthcare systems including misinformation generation, lack of real-time medical understanding, multimodal integration complexity, healthcare data privacy concerns, and infrastructure dependency. Future research directions such as explainable AI, multilingual healthcare systems, cloud-based healthcare platforms, and real-time healthcare analytics are also explored.