Wisit Cheungpasitporn, Charat Thongprayoon, Kianoush B Kashani
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LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation.</p><p><strong>Summary: </strong>The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.</p>","PeriodicalId":10851,"journal":{"name":"Current Opinion in Critical Care","volume":" ","pages":"533-541"},"PeriodicalIF":3.5000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advances in critical care nephrology through artificial intelligence.\",\"authors\":\"Wisit Cheungpasitporn, Charat Thongprayoon, Kianoush B Kashani\",\"doi\":\"10.1097/MCC.0000000000001202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose of review: </strong>This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology.</p><p><strong>Recent findings: </strong>AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24-48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation.</p><p><strong>Summary: </strong>The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. 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引用次数: 0
摘要
综述的目的:这篇综述探讨了人工智能(AI),尤其是机器学习(ML)和大型语言模型(LLM)的变革性进步、潜在应用和对重症肾脏病学的影响:人工智能算法已证明有能力加强早期检测、改善风险预测、个性化治疗策略,并支持急性肾损伤(AKI)管理的临床决策过程。ML 模型可在血清肌酐水平变化前 24-48 小时预测 AKI,而人工智能则有可能识别出具有不同临床特征和结果的 AKI 亚型,从而进行有针对性的干预。LLM 和生成式人工智能为自动生成临床笔记提供了机会,并提供了宝贵的患者教育材料,使患者能够更好地了解自己的病情和治疗方案。要充分发挥人工智能在重症肾脏病学中的潜力,必须正视人工智能实施过程中的局限性和挑战,包括数据质量问题、伦理考虑以及严格验证的必要性。虽然人工智能在改善患者预后方面大有可为,但其成功实施需要持续的培训、教育以及肾脏病专家、重症监护专家和人工智能专家之间的合作。
Advances in critical care nephrology through artificial intelligence.
Purpose of review: This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology.
Recent findings: AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24-48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation.
Summary: The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.
期刊介绍:
Current Opinion in Critical Care delivers a broad-based perspective on the most recent and most exciting developments in critical care from across the world. Published bimonthly and featuring thirteen key topics – including the respiratory system, neuroscience, trauma and infectious diseases – the journal’s renowned team of guest editors ensure a balanced, expert assessment of the recently published literature in each respective field with insightful editorials and on-the-mark invited reviews.