LuminaURO: A comprehensive Artificial Intelligence Driven Assistant for enhancing urological diagnostics and patient care

Abstract

Aim: This study aims to develop and validate LuminaURO, a Retrieval-Augmented Generation (RAG)-based AI Assistant specifically designed for urological healthcare, addressing the limitations of conventional Large Language Models (LLMs) in healthcare applications. Methods: We developed LuminaURO using a specialized repository of urological documents and implemented a novel pooling methodology to search multilingual documents and aggregate information for response generation. The system was evaluated using multiple similarity algorithms (OESM, Spacy, T5, and BERTScore) and expert assessment by urologists (n=3). Results: LuminaURO generates responses within 8-15 seconds from multilingual documents and enhances user interaction by providing two contextually relevant follow-up questions per query. The architecture demonstrates significant improvements in search latency, memory requirements, and similarity metrics compared to state-of-the-art approaches. Validation shows similarity scores of 0.6756, 0.7206, 0.9296, 0.9223, and 0.9183 for English responses, and 0.6686, 0.7166, 0.8119, 0.9220, 0.9315, and 0.9086 for Turkish responses. Expert evaluation by urologists revealed similarity scores of 0.9444 and 0.9408 for English and Turkish responses, respectively. Conclusion: LuminaURO successfully addresses the limitations of conventional LLM implementations in healthcare by utilizing specialized urological documents and our innovative pooling methodology for multi-language document processing. The high similarity scores across multiple evaluation metrics and strong expert validation confirm the system’s effectiveness in providing accurate and relevant urological information. Future research will focus on expanding this approach to other medical specialties, with the ultimate goal of developing LuminaHealth, a comprehensive healthcare assistant covering all medical domains.

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