Context-Aware Retrieval-Augmented Generation for Artificial Intelligence in Urology.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Urology Research

Background:

• Artificial intelligence (AI) is increasingly utilized in healthcare for complex medical query interpretation.
• Conventional AI models often produce inaccurate or irrelevant responses, known as hallucinations, posing risks to patient safety.
• A novel retrieval-augmented generation (RAG) framework is proposed to address these limitations in the urology domain.

Purpose of the Study:

• To develop and evaluate a modified RAG framework specifically for the urology domain.
• To enhance the contextual relevance and accuracy of AI-generated responses in medical queries.
• To mitigate the issue of AI hallucinations in clinical applications.

Main Methods:

• Developed a context-aware RAG system using PubMedBERT embeddings and a Pinecone vector database for urological literature.
• Integrated named entity recognition for domain-specific query filtering and dynamic memory for contextual flow.
• Employed the LLaMA3-8B model for response generation and Deepseek-R1 for evaluation on a custom urology dataset.

Main Results:

• The RAG-enhanced framework demonstrated a significant reduction in AI hallucinations.
• Responses were more contextually relevant and evidence-based compared to baseline models.
• Achieved an 89% performance improvement in generating medically appropriate answers, with enhanced precision and reliability.

Conclusions:

• The RAG-enhanced system shows strong potential for clinical application in urology, providing trustworthy and context-aware responses.
• Effectively addresses key challenges in medical AI, including hallucination mitigation and domain relevance.
• Future research will focus on reducing inference latency and enhancing automated validation processes.