• Tezcan Ozrazgat-Baslanti, Tyler J Loftus, Yuanfang Ren, Matthew M Ruppert, and Azra Bihorac.
• Department of Medicine.
• Curr Opin Crit Care. 2021 Dec 1; 27 (6): 560572560-572.

Purpose Of ReviewAcute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients.Recent FindingsAmong recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies.SummaryUse of consensus criteria, standard definitions and common data models could facilitate access to machine learning-ready data sets for external validation. The lack of interpretability, explainability, fairness and transparency of artificial intelligence models hinder their entrustment and clinical implementation; compliance with standardized reporting guidelines can mitigate these challenges.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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