重症监护肾脏病学中的生成人工智能:应用与未来展望》。

IF 2.2 3区 医学 Q3 HEMATOLOGY
Wisit Cheungpasitporn, Charat Thongprayoon, Claudio Ronco, Kianoush B Kashani
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引用次数: 0

摘要

背景:生成式人工智能(AI)正在迅速改变医疗保健的各个方面,包括重症肾病学。大型语言模型(LLMs)是生成式人工智能的一项关键技术,在加强患者护理、简化工作流程和推进该领域研究方面大有可为。摘要:这篇综述分析了生成式人工智能在重症肾脏病学中的当前应用和未来前景。最近的研究证明了 LLM 在诊断准确性、临床推理和持续肾脏替代疗法(CRRT)警报故障排除方面的能力。随着多代理模型和自动化时代的到来,将生成式人工智能融入重症肾脏病学有望改善患者护理、优化临床流程并加速研究。然而,要在临床实践中负责任地应用这些技术,必须仔细考虑其伦理影响并不断完善。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,我们还探讨了人工智能应用所面临的挑战和局限性,如隐私问题、潜在的偏见以及人工监督的必要性。主要信息:(i) LLM 在提高重症肾脏病学的诊断准确性、临床推理和 CRRT 警报故障排除方面显示出潜力。(ii) 在肾脏病学领域,生成式人工智能在患者教育、文献综述和学术写作方面的应用前景广阔。(iii) 将人工智能融入电子健康记录和临床工作流程,为改善患者护理和研究工作带来了机遇和挑战。(iv) 解决伦理问题、确保数据隐私和保持人工监督对于在重症肾脏病学中负责任地实施人工智能至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative AI in Critical Care Nephrology: Applications and Future Prospects.

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.

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来源期刊
Blood Purification
Blood Purification 医学-泌尿学与肾脏学
CiteScore
5.80
自引率
3.30%
发文量
69
审稿时长
6-12 weeks
期刊介绍: Practical information on hemodialysis, hemofiltration, peritoneal dialysis and apheresis is featured in this journal. Recognizing the critical importance of equipment and procedures, particular emphasis has been placed on reports, drawn from a wide range of fields, describing technical advances and improvements in methodology. Papers reflect the search for cost-effective solutions which increase not only patient survival but also patient comfort and disease improvement through prevention or correction of undesirable effects. Advances in vascular access and blood anticoagulation, problems associated with exposure of blood to foreign surfaces and acute-care nephrology, including continuous therapies, also receive attention. Nephrologists, internists, intensivists and hospital staff involved in dialysis, apheresis and immunoadsorption for acute and chronic solid organ failure will find this journal useful and informative. ''Blood Purification'' also serves as a platform for multidisciplinary experiences involving nephrologists, cardiologists and critical care physicians in order to expand the level of interaction between different disciplines and specialities.
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