Name: Advanced RAG Chatbot Assistant for Healthcare Patient Records
Start: 2025-11-27T19:00:00-05:00
End: 2025-11-27T20:00:00-05:00

**Topic**:Advanced RAG Chatbot Assistant for Healthcare Patient Records

**Description**: This paper presents “Talking to Patient Records,” an advanced Retrieval-Augmented Generation (RAG) chatbot assistant designed to enhance healthcare information retrieval. The system integrates natural language processing with domain-specific knowledge bases to allow clinicians, researchers, and administrators to query patient records conversationally. By combining large language models with RAG techniques, the chatbot delivers accurate, context-aware, and secure responses, reducing the time required to locate critical patient information. The study outlines the system’s architecture, implementation, and potential applications in clinical decision support, patient engagement, and healthcare data management.:

* We’re excited to feature Ernest Bonat, a seasoned computer scientist and educator with nearly three decades of experience in database systems, programming, and AI, at our upcoming session: “Advanced RAG Chatbot Assistant for Healthcare Patient Records.
* With 28+ years designing and deploying complex data systems across industries, Ernest has led and delivered advanced AI and data science projects, specializing in NLP, machine learning, and enterprise-grade data applications. As an educator, he has shaped future technologists through his teaching and mentorship at leading institutions..
* In this session, Ernest will explore how domain-specific RAG chatbot systems can revolutionize how clinicians and researchers interact with patient data—making information retrieval faster, smarter, and more secure..

Who Should Attend:

This session will introduce a cutting-edge RAG-powered chatbot system designed to transform how healthcare professionals interact with patient records. By merging LLMs with domain-specific knowledge bases, the system enables faster, more accurate, and secure data access supporting clinical decision-making and patient engagement..

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