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

Yıl 2025, Cilt: 30 Sayı: 2, 278 - 294, 29.05.2025

Tuncay Soylu , İbrahim Topçu , Muhammet İhsan Karaman , Esra Melis Tuzcu , Abdullah Harun Kınık , Mustafa Sacit Güneren , Zeynep Salman , Perihan Demir , Beyzanur Kaç

https://doi.org/10.21673/anadoluklin.1653335

Öz

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.

Anahtar Kelimeler

Artificial intelligence decision support systems natural language processing medical informatics urology

Kaynakça

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  • Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117.
  • He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 770–778.
  • Graves A, Mohamed AR, Hinton G. Speech recognition with deep recurrent neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2013. p. 6645–6649.
  • Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT); 2019. p. 4171–4186.
  • Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS); 2017. p. 5998–6008.
  • Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems (NeurIPS); 2015. p. 649–657.
  • Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT); 2016. p. 1480–1489.
  • Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NeurIPS); 2014. p. 3104–3112.
  • Rajpurkar P, Zhang J, Lopyrev K, Liang P. SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2016. p. 2383–2392.
  • Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. In: Advances in Neural Information Processing Systems (NeurIPS); 2020. p. 1877–1901.
  • Chowdhery A, Narang S, Devlin J, et al. PaLM: scaling language modeling with pathways. J Mach Learn Res. 2023;24(240):1–113.
  • Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. OpenAI Blog. 2019;1(8):9.
  • Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. 2021.
  • Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res. 2020;21(140):1–67.
  • Wei J, Tay Y, Bommasani R, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682. 2022.
  • Benary M, Wang XD, Schmidt M, et al. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open. 2023;6(11):e2343689.
  • Eckrich J, Ellinger J, Cox A, et al. Urology consultants versus large language models: potentials and hazards for medical advice in urology. BJUI Compass. 2024;5(5):438–44.
  • Lu Z, Peng Y, Cohen T, Ghassemi M, Weng C, Tian S. Large language models in biomedicine and health: current research landscape and future directions. J Am Med Inform Assoc. 2024;31(9):1801-11.
  • Nerella S, Bandyopadhyay S, Zhang J, et al. Transformers and large language models in healthcare: a review. Artif Intell Med. 2024;154:102900.
  • Alonso I, Oronoz M, Agerri R. MedExpQA: multilingual benchmarking of large language models for medical question answering. Artif Intell Med. 2024;155:102938.
  • Kaplan J, McCandlish S, Henighan T, et al. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. 2020.
  • Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT); 2021. p. 610–623.
  • Ji Z, Lee N, Frieske R, et al. Survey of hallucination in natural language generation. ACM Comput Surv. 2023;55(12):1–38.
  • Weidinger L, Mellor J, Rauh M, et al. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359. 2021.
  • Thoppilan R, De Freitas D, Hall J, et al. LaMDA: language models for dialog applications. arXiv preprint arXiv:2201.08239. 2022.
  • Zhou H, Liu F, Gu B, et al. A survey of large language models in medicine: progress, application, and challenge. arXiv preprint arXiv:2311.05112. 2023.
  • Fan A, Bhosale S, Schwenk H, et al. Beyond English-centric multilingual machine translation. J Mach Learn Res. 2021;22:1–48.
  • Wang X, Wei J, Schuurmans D, et al. Self-consistency improves chain of thought reasoning in language models. In: Proceedings of the 11th International Conference on Learning Representations (ICLR); 2023.
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  • McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.
  • Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58.
  • Fox S, Duggan M. Health online 2013. Pew Research Center [Internet]. 2013 [cited 2024 Nov 5]. Available from: https://www.ordinedeimedici.com/documenti/Docs7-cybercondria-PIP-HealthOnline.pdf
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  • RAG [Internet]. Available from: https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview
  • Peng C, Yang X, Chen A, et al. A study of generative large language model for medical research and healthcare. NPJ Digit Med. 2023;6(1):210.
  • Long C, Subburam D, Lowe K, et al. ChatENT: augmented large language model for expert knowledge retrieval in otolaryngology–head and neck surgery. Otolaryngol Head Neck Surg. 2024;171(4):1042–51.
  • Luo MJ, Pang J, Bi S, et al. Development and evaluation of a retrieval-augmented large language model framework for ophthalmology. JAMA Ophthalmol. 2024;142(9):798–805.
  • Zheng C, Ye H, Guo J, et al. Development and evaluation of a large language model of ophthalmology in Chinese. Br J Ophthalmol. 2024;108(10):1390–7.
  • Li Y, Li Z, Zhang K, Dan R, Jiang S, Zhang Y. ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge. Cureus. 2023;15(6):e40895.
  • Huang AS, Hirabayashi K, Barna L, Parikh D, Pasquale LR. Assessment of a large language model’s responses to questions and cases about glaucoma and retina management. JAMA Ophthalmol. 2024;142(4):371–5.
  • Haider SA, Pressman SM, Borna S, et al. Evaluating large language model (LLM) performance on established breast classification systems. Diagnostics (Basel). 2024;14(14):1491.
  • Kim J, Leonte KG, Chen ML, et al. Large language models outperform mental and medical health care professionals in identifying obsessive-compulsive disorder. NPJ Digit Med. 2024;7(1):193.
  • Upadhyaya D, Shaikh A, Cakir G, et al. A 360 degree view for large language models: early detection of amblyopia in children using multi-view eye movement recordings. medRxiv [Preprint]. 2024.
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  • Ge J, Sun S, Owens J, et al. Development of a liver disease-specific large language model chat interface using retrieval-augmented generation. Hepatology. 2024;80(5):1158-68.
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  • Kresevic S, Giuffrè M, Ajcevic M, Accardo A, Crocè LS, Shung DL. Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework. NPJ Digit Med. 2024;7(1):102.
  • Kozaily E, Geagea M, Akdogan ER, et al. Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients’ questions about heart failure. Int J Cardiol. 2024;408:132115.
  • Bernstein IA, Zhang Y, Govil D, et al. Comparison of ophthalmologist and large language model chatbot responses to online patient eye care questions. JAMA Netw Open. 2023;6(8):e2330320.
  • Yalamanchili A, Sengupta B, Song J, et al. Quality of large language model responses to radiation oncology patient care questions. JAMA Netw Open. 2024;7(4):e244630.
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LuminaURO: Ürolojik tanı ve hasta bakımını geliştirmek için kapsamlı bir Yapay Zeka Destekli Asistan

Yıl 2025, Cilt: 30 Sayı: 2, 278 - 294, 29.05.2025

Tuncay Soylu , İbrahim Topçu , Muhammet İhsan Karaman , Esra Melis Tuzcu , Abdullah Harun Kınık , Mustafa Sacit Güneren , Zeynep Salman , Perihan Demir , Beyzanur Kaç

https://doi.org/10.21673/anadoluklin.1653335

Öz

Amaç: Bu çalışma, ürolojik sağlık hizmetleri için özel olarak tasarlanmış, Erişim-Güçlendirilmiş Üretim (RAG) tabanlı bir yapay zeka asistanı olan LuminaURO’yu geliştirmeyi ve doğrulamayı amaçlamaktadır. Bu sistem, sağlık uygulamalarında geleneksel Büyük Dil Modellerinin (LLM) sınırlamalarını ele almaktadır.

Yöntemler: LuminaURO’yu ürolojik dokümanların özel bir deposunu kullanarak geliştirdik ve çok dilli dokümanları aramak ve yanıt üretimi için bilgileri toplamak amacıyla yenilikçi bir havuzlama metodolojisi uyguladık. Sistem, çoklu benzerlik algoritmaları (OESM, Spacy, T5 ve BERTScore) ve ürologlar tarafından uzman değerlendirmesi (n=3) kullanılarak değerlendirildi.

Bulgular: LuminaURO, çok dilli dokümanlardan 8-15 saniye içinde yanıtlar üretmekte ve her sorgu için bağlamsal olarak ilgili iki takip sorusu sunarak kullanıcı etkileşimini geliştirmektedir. Mimari, son teknoloji yaklaşımlara kıyasla arama gecikmesi, bellek gereksinimleri ve benzerlik metrikleri açısından önemli iyileştirmeler göstermektedir. Doğrulama, İngilizce yanıtlar için 0,6756, 0,7206, 0,9296, 0,9223 ve 0,9183, Türkçe yanıtlar için ise 0,6686, 0,7166, 0,8119, 0,9220, 0,9315 ve 0,9086 benzerlik puanları göstermektedir. Ürologlar tarafından yapılan uzman değerlendirmesi, sırasıyla İngilizce ve Türkçe yanıtlar için 0,9444 ve 0,9408 benzerlik puanları ortaya koymuştur.

Sonuç: LuminaURO, özel ürolojik dokümanları ve çok dilli doküman işleme için yenilikçi havuzlama metodolojimizi kullanarak sağlık hizmetlerinde geleneksel LLM uygulamalarının sınırlamalarını başarıyla ele almaktadır. Çoklu değerlendirme metriklerinde elde edilen yüksek benzerlik puanları ve güçlü uzman doğrulaması, sistemin doğru ve ilgili ürolojik bilgileri sağlama konusundaki etkinliğini teyit etmektedir. Gelecekteki araştırmalar, bu yaklaşımı diğer tıbbi uzmanlık alanlarına genişletmeye odaklanacak ve nihai hedef olarak tüm tıbbi alanları kapsayan kapsamlı bir sağlık asistanı olan LuminaHealth’i geliştirmek olacaktır.

Anahtar Kelimeler

Doğal lisan işleme karar destek sistemleri tıbbi bilişim üroloji yapay zekâ

Kaynakça

  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
  • Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117.
  • He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 770–778.
  • Graves A, Mohamed AR, Hinton G. Speech recognition with deep recurrent neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2013. p. 6645–6649.
  • Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT); 2019. p. 4171–4186.
  • Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS); 2017. p. 5998–6008.
  • Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems (NeurIPS); 2015. p. 649–657.
  • Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT); 2016. p. 1480–1489.
  • Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NeurIPS); 2014. p. 3104–3112.
  • Rajpurkar P, Zhang J, Lopyrev K, Liang P. SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2016. p. 2383–2392.
  • Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. In: Advances in Neural Information Processing Systems (NeurIPS); 2020. p. 1877–1901.
  • Chowdhery A, Narang S, Devlin J, et al. PaLM: scaling language modeling with pathways. J Mach Learn Res. 2023;24(240):1–113.
  • Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. OpenAI Blog. 2019;1(8):9.
  • Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. 2021.
  • Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res. 2020;21(140):1–67.
  • Wei J, Tay Y, Bommasani R, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682. 2022.
  • Benary M, Wang XD, Schmidt M, et al. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open. 2023;6(11):e2343689.
  • Eckrich J, Ellinger J, Cox A, et al. Urology consultants versus large language models: potentials and hazards for medical advice in urology. BJUI Compass. 2024;5(5):438–44.
  • Lu Z, Peng Y, Cohen T, Ghassemi M, Weng C, Tian S. Large language models in biomedicine and health: current research landscape and future directions. J Am Med Inform Assoc. 2024;31(9):1801-11.
  • Nerella S, Bandyopadhyay S, Zhang J, et al. Transformers and large language models in healthcare: a review. Artif Intell Med. 2024;154:102900.
  • Alonso I, Oronoz M, Agerri R. MedExpQA: multilingual benchmarking of large language models for medical question answering. Artif Intell Med. 2024;155:102938.
  • Kaplan J, McCandlish S, Henighan T, et al. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. 2020.
  • Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT); 2021. p. 610–623.
  • Ji Z, Lee N, Frieske R, et al. Survey of hallucination in natural language generation. ACM Comput Surv. 2023;55(12):1–38.
  • Weidinger L, Mellor J, Rauh M, et al. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359. 2021.
  • Thoppilan R, De Freitas D, Hall J, et al. LaMDA: language models for dialog applications. arXiv preprint arXiv:2201.08239. 2022.
  • Zhou H, Liu F, Gu B, et al. A survey of large language models in medicine: progress, application, and challenge. arXiv preprint arXiv:2311.05112. 2023.
  • Fan A, Bhosale S, Schwenk H, et al. Beyond English-centric multilingual machine translation. J Mach Learn Res. 2021;22:1–48.
  • Wang X, Wei J, Schuurmans D, et al. Self-consistency improves chain of thought reasoning in language models. In: Proceedings of the 11th International Conference on Learning Representations (ICLR); 2023.
  • ChatGPT [Internet]. Available from: https://chatgpt.com
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  • Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31.
  • McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.
  • Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58.
  • Fox S, Duggan M. Health online 2013. Pew Research Center [Internet]. 2013 [cited 2024 Nov 5]. Available from: https://www.ordinedeimedici.com/documenti/Docs7-cybercondria-PIP-HealthOnline.pdf
  • Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLoS Digit Health. 2023;2(2):e0000198.
  • Lee J, Yoon W, Kim S, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–40.
  • RAG [Internet]. Available from: https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview
  • Peng C, Yang X, Chen A, et al. A study of generative large language model for medical research and healthcare. NPJ Digit Med. 2023;6(1):210.
  • Long C, Subburam D, Lowe K, et al. ChatENT: augmented large language model for expert knowledge retrieval in otolaryngology–head and neck surgery. Otolaryngol Head Neck Surg. 2024;171(4):1042–51.
  • Luo MJ, Pang J, Bi S, et al. Development and evaluation of a retrieval-augmented large language model framework for ophthalmology. JAMA Ophthalmol. 2024;142(9):798–805.
  • Zheng C, Ye H, Guo J, et al. Development and evaluation of a large language model of ophthalmology in Chinese. Br J Ophthalmol. 2024;108(10):1390–7.
  • Li Y, Li Z, Zhang K, Dan R, Jiang S, Zhang Y. ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge. Cureus. 2023;15(6):e40895.
  • Huang AS, Hirabayashi K, Barna L, Parikh D, Pasquale LR. Assessment of a large language model’s responses to questions and cases about glaucoma and retina management. JAMA Ophthalmol. 2024;142(4):371–5.
  • Haider SA, Pressman SM, Borna S, et al. Evaluating large language model (LLM) performance on established breast classification systems. Diagnostics (Basel). 2024;14(14):1491.
  • Kim J, Leonte KG, Chen ML, et al. Large language models outperform mental and medical health care professionals in identifying obsessive-compulsive disorder. NPJ Digit Med. 2024;7(1):193.
  • Upadhyaya D, Shaikh A, Cakir G, et al. A 360 degree view for large language models: early detection of amblyopia in children using multi-view eye movement recordings. medRxiv [Preprint]. 2024.
  • Dou Y, Huang Y, Zhao X, et al. ShennongMGS: an LLM-based Chinese medication guidance system. ACM Trans Manag Inf Syst. 2024;16(2):1-14.
  • Chang JJ, Chang EY. SocraHealth: enhancing medical diagnosis and correcting historical records. In: Proceedings of the 10th International Conference on Computational Science and Computational Intelligence (CSCI); 2023.
  • Ge J, Sun S, Owens J, et al. Development of a liver disease-specific large language model chat interface using retrieval-augmented generation. Hepatology. 2024;80(5):1158-68.
  • Mukherjee P, Hou B, Lanfredi RB, Summers RM. Feasibility of using the privacy-preserving large language model Vicuna for labeling radiology reports. Radiology. 2023;309:e231147.
  • Kresevic S, Giuffrè M, Ajcevic M, Accardo A, Crocè LS, Shung DL. Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework. NPJ Digit Med. 2024;7(1):102.
  • Kozaily E, Geagea M, Akdogan ER, et al. Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients’ questions about heart failure. Int J Cardiol. 2024;408:132115.
  • Bernstein IA, Zhang Y, Govil D, et al. Comparison of ophthalmologist and large language model chatbot responses to online patient eye care questions. JAMA Netw Open. 2023;6(8):e2330320.
  • Yalamanchili A, Sengupta B, Song J, et al. Quality of large language model responses to radiation oncology patient care questions. JAMA Netw Open. 2024;7(4):e244630.
  • Warren CJ, Edmonds VS, Payne NG, et al. Prompt matters: evaluation of large language model chatbot responses related to Peyronie’s disease. Sex Med. 2024;12(4):qfae055.
  • OpenAI [Internet]. 2024 [cited 2024 Dec 16]. Available from: https://platform.openai.com/docs/overview
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  • Streamlit [Internet]. 2024. Available from: https://streamlit.io
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  • Papineni K, Roukos S, Ward T, Zhu WJ. BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL); 2002. p. 311–318.
  • Banerjee S, Lavie A. METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization; 2005. p. 65–72.
  • Lin CY, Och FJ. Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04); 2004. p. 605–612.
  • Neelakantan A, Xu T, Puri R, et al. Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201. 2022.
  • spaCy [Internet]. [cited 2025 Feb 22]. Available from: https://spacy.io/
Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Üroloji, Sağlık Hizmetleri ve Sistemleri (Diğer)
Bölüm ORJİNAL MAKALE
Yazarlar

Tuncay Soylu 0000-0001-7595-8879

İbrahim Topçu 0000-0001-7685-8597

Muhammet İhsan Karaman 0000-0001-5700-0835

Esra Melis Tuzcu 0009-0003-2194-1563

Abdullah Harun Kınık 0000-0001-8121-1480

Mustafa Sacit Güneren 0009-0000-2411-1096

Zeynep Salman 0000-0002-9864-3156

Perihan Demir 0000-0002-0490-8353

Beyzanur Kaç 0000-0002-7461-1942

Yayımlanma Tarihi 29 Mayıs 2025
Gönderilme Tarihi 7 Mart 2025
Kabul Tarihi 28 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 30 Sayı: 2

Kaynak Göster

Vancouver Soylu T, Topçu İ, Karaman Mİ, Tuzcu EM, Kınık AH, Güneren MS, Salman Z, Demir P, Kaç B. LuminaURO: A comprehensive Artificial Intelligence Driven Assistant for enhancing urological diagnostics and patient care. Anadolu Klin. 2025;30(2):278-94.
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