BACKGROUND Generative artificial intelligence (AI) is rapidly transforming various aspects of healthcare, including critical care nephrology. Large language models (LLMs), a key technology in generative AI, show promise in enhancing patient care, streamlining workflows, and advancing research in this field. SUMMARY This review analyzes the current applications and future prospects of generative AI in critical care nephrology. Recent studies demonstrate the capabilities of LLMs in diagnostic accuracy, clinical reasoning, and continuous renal replacement therapy (CRRT) alarm troubleshooting. As we enter an era of multiagent models and automation, the integration of generative AI into critical care nephrology holds promise for improving patient care, optimizing clinical processes, and accelerating research. However, careful consideration of ethical implications and continued refinement of these technologies are essential for their responsible implementation in clinical practice. This review explores the current and potential applications of generative AI in nephrology, focusing on clinical decision support, patient education, research, and medical education. Additionally, we examine the challenges and limitations of AI implementation, such as privacy concerns, potential bias, and the necessity for human oversight. KEY MESSAGES (i) LLMs have shown potential in enhancing diagnostic accuracy, clinical reasoning, and CRRT alarm troubleshooting in critical care nephrology. (ii) Generative AI offers promising applications in patient education, literature review, and academic writing within the field of nephrology. (iii) The integration of AI into electronic health records and clinical workflows presents both opportunities and challenges for improving patient care and research. (iv) Addressing ethical concerns, ensuring data privacy, and maintaining human oversight are crucial for the responsible implementation of AI in critical care nephrology.

中文翻译：

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重症监护肾病学中的生成人工智能：应用和未来前景。


背景技术生成人工智能（AI）正在迅速改变医疗保健的各个方面，包括重症监护肾病学。大语言模型 ( LLMs ) 是生成人工智能的一项关键技术，在增强患者护理、简化工作流程和推进该领域的研究方面显示出前景。摘要本文分析了生成式人工智能在重症监护肾脏病学中的当前应用和未来前景。最近的研究证明了LLMs在诊断准确性、临床推理和连续肾脏替代治疗 (CRRT) 警报故障排除方面的能力。随着我们进入多智能体模型和自动化时代，将生成人工智能集成到重症监护肾病学中有望改善患者护理、优化临床流程和加速研究。然而，仔细考虑伦理影响并不断完善这些技术对于在临床实践中负责任地实施至关重要。本综述探讨了生成式人工智能在肾脏病学中的当前和潜在应用，重点关注临床决策支持、患者教育、研究和医学教育。此外，我们还研究了人工智能实施的挑战和局限性，例如隐私问题、潜在偏见以及人类监督的必要性。关键信息 (i) LLMs已显示出在提高重症监护肾病学诊断准确性、临床推理和 CRRT 警报故障排除方面的潜力。 (ii) 生成式人工智能在肾脏病学领域的患者教育、文献综述和学术写作方面提供了有前景的应用。 (iii) 将人工智能整合到电子健康记录和临床工作流程中，为改善患者护理和研究带来了机遇和挑战。 (iv) 解决伦理问题、确保数据隐私和维持人类监督对于在重症监护肾病学中负责任地实施人工智能至关重要。