• Wisit Cheungpasitporn, Charat Thongprayoon, and Kianoush B Kashani.
• Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester.
• Curr Opin Crit Care. 2024 Dec 1; 30 (6): 533541533-541.

Purpose Of ReviewThis 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 FindingsAI 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.SummaryThe 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.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

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