Pantilimonescu Theodor Florin, Damian Costin, Radu Viorel Dragos, Hogea Maximilian, Costachescu Oana Andreea, Onofrei Pavel, Toma Bogdan, Zelinschi Denisa, Roca Iulia Cristina, Ursu Ramona Gabriela, Iancu Luminita Smaranda, Serban Ionela Lacramioara
Department of Physiology, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania.
Department of Preventive Medicine and Interdisciplinarity (IX)-Microbiology, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania.
J Clin Med. 2025 Jul 12;14(14):4942. doi: 10.3390/jcm14144942.
Urinary tract infections (UTIs) are a common pathology worldwide, frequently associated with kidney stones. We aimed to determine how artificial intelligence (AI) could assist and enhance human medical activities in this field. We performed a search in PubMed using different sets of keywords. When using the keywords "AI, artificial intelligence, urinary tract infections, ()", we identified 16 papers, 12 of which fulfilled our research criteria. When using the keywords "urolithiasis, AI, artificial intelligence", we identified 72 results, 30 of which were suitable for analysis. We identified that AI/machine learning can be used to detect Gram-negative bacilli involved in UTIs in a fast and accurate way and to detect antibiotic-resistant genes in . The most frequent AI applications for urolithiasis can be summarized into three categories: The first category relates to patient follow-up, trying to improve physical and medical conditions after specific urologic surgical procedures. The second refers to urinary stone disease (USD), focused on stone evaluation, using different AI and machine learning systems, regarding the stone's composition in terms of uric acid, its dimensions, its volume, and its speed of detection. The third category comprises the comparison of the ChatGPT-4, Bing AI, Grok, Claude, and Perplexity chatbots in different applications for urolithiasis. ChatGPT-4 has received the most positive evaluations. In conclusion, the impressive number of papers published on different applications of AI in UTIs and urology suggest that machine learning will be exploited effectively in the near future to optimize patient follow-up, diagnosis, and treatment.
尿路感染（UTIs）是全球常见的病理状况，常与肾结石相关。我们旨在确定人工智能（AI）如何在该领域协助并增强人类医疗活动。我们在PubMed上使用不同的关键词组进行了检索。当使用关键词“AI、人工智能、尿路感染、（）”时，我们识别出16篇论文，其中12篇符合我们的研究标准。当使用关键词“尿石症、AI、人工智能”时，我们识别出72个结果，其中30个适合进行分析。我们发现AI/机器学习可用于快速准确地检测与尿路感染相关的革兰氏阴性杆菌，并检测其中的抗生素耐药基因。尿石症最常见的AI应用可归纳为三类：第一类涉及患者随访，试图改善特定泌尿外科手术后的身体和医疗状况。第二类指尿石症（USD），使用不同的AI和机器学习系统，专注于结石评估，涉及尿酸方面的结石成分、尺寸、体积及其检测速度。第三类包括在尿石症的不同应用中对ChatGPT-4、必应AI、Grok、Claude和Perplexity聊天机器人的比较。ChatGPT-4获得了最积极的评价。总之，关于AI在尿路感染和泌尿外科不同应用方面发表的论文数量可观，这表明机器学习在不久的将来将被有效利用，以优化患者随访、诊断和治疗。
