Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester.
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic Health System, Mankato.
Curr Opin Crit Care. 2024 Dec 1;30(6):533-541. doi: 10.1097/MCC.0000000000001202. Epub 2024 Aug 30.
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.
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.
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.
目的综述：本综述探讨了人工智能（AI），特别是机器学习（ML）和大型语言模型（LLMs）在重症肾科领域的变革性进展、潜在应用和影响。
最新发现：AI 算法已经证明了在急性肾损伤（AKI）管理中增强早期检测、改善风险预测、个性化治疗策略和支持临床决策过程的能力。ML 模型可以在血清肌酐水平变化前 24-48 小时预测 AKI，并且 AI 有可能识别具有不同临床特征和结局的 AKI 亚表型，以便进行针对性干预。LLMs 和生成式 AI 为自动化临床记录生成提供了机会，并提供了有价值的患者教育材料，使患者能够更好地了解自己的病情和治疗选择。为了充分利用 AI 在重症肾科中的潜力，必须应对 AI 实施的限制和挑战，包括数据质量、伦理考虑以及严格验证的必要性。
总结：AI 在重症肾科中的整合有可能彻底改变 AKI 和持续肾脏替代治疗的管理。虽然 AI 在改善患者预后方面具有巨大的潜力，但要成功实施，需要肾病学家、重症监护医生和 AI 专家之间进行持续的培训、教育和合作。
