AI Medical Health Assistant: Rag Approach

Abstract

This paper presents the development of an AI Medical Health Assistant using the Retrieval-Augmented Generation (RAG) approach to improve patient engagement, care delivery, and accessibility. The system combines advanced NLP and machine learning to offer real-time diagnostics, patient education, and personalized healthcare recommendations. By integrating retrieval-based methods with generative models, it delivers accurate, context-aware medical information. The paper outlines the system's architecture, data sources, retrieval and generation processes, and evaluation metrics. It also addresses challenges like accessibility, cost, and patient outcomes, while examining its impact on clinical workflows and future research. The RAGbased assistant achieves an F1 score of 84.5%, outperforming LLM-only models by 12.4% and reducing inference time by 22%. By retrieving current medical guidelines (e.g., FDA/WHO), it cuts errors by 19% and enhances clinician trust through source transparency.

Ähnliche Arbeiten