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Yayın LuminaURO: a comprehensive Artificial Intelligence Driven Assistant for enhancing urological diagnostics and patient care(Hayat Sağlık ve Sosyal Hizmetler Vakfı, 2025-05-29) Soylu, Tuncay; Topçu, İbrahim; Karaman, Muhammet İhsan; Tuzcu, Esra Melis; Kınık, Abdullah Harun; Güneren, Mustafa Sacit; Salman, Zeynep; Demir, Perihan; Beyzanur, KaçAim: 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 multilanguage 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.Yayın AI in architectural education: rethinking studio culture(Atatürk Üniversitesi, 2025-09-20) Karadağ, DeryaThis article examines the pedagogical transformations emerging in architectural education through a conceptual and critical perspective focused on human–AI co-creativity. Co-creativity specifically refers to collaborations between human designers and artificial intelligence, in contrast to broader notions of collaborative creativity. The paper argues that AI functions not merely as a technical instrument, but as a co-creative partner that reshapes studio culture, authorship, and creative work. Drawing on selected studio-based cases, the study explores how AI-supported workflows influence ideation, representation, critique culture, prompt literacy, and ethical reasoning. Thematically, it engages with concepts such as cognitive augmentation and conceptual ambiguity to demonstrate how design pedagogy is evolving in response to intelligent systems. Rather than viewing AI as a generative tool alone, the article positions it as an epistemic and ethical agent that prompts a rethinking of studio environments as cultural and pedagogical spaces. Methodologically, the study adopts a casebased approach, analyzing selected 16 design studios in which AI was integrated into early-stage ideation, feedback sessions, and conceptual development. These cases extent strategies from prompt-driven speculation to hybrid critique practices, revealing a dynamic landscape of experimentation and adaptation. The findings suggest that AI can foster deeper conceptual inquiry, student reflection, and new modalities of authorship and collaboration. Eventually, the study underscores the need for reflexive pedagogical frameworks that integrate AI meaningfully enhancing, rather than displacing, human creativity.
