| Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions |
Inyong Jeong1, Nam-Jun Cho2, Se-Jin Ahn1, Hwamin Lee1, and Hyo-Wook Gil2
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1Department of Medical Informatics, College of Medicine, Korea University, Seoul, Korea
2Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Korea |
Corresponding Author:
Hyo-Wook Gil , Tel: +82-41-570-3682, Fax: +82-41-574-5762, Email: hwgil@schmc.ac.kr
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Received: March 21, 2024; Revised: April 26, 2024; Accepted: June 7, 2024. |
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| Abstract |
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Acute kidney injury (AKI) is a significant health challenge associated with adverse patient outcomes and substantial economic burdens. Many authors have sought to prevent and predict AKI. Here, we comprehensively review recent advances in the use of artificial intelligence (AI) to predict AKI, and the associated challenges. Although AI may detect AKI early and predict prognosis, integration of AI-based systems into clinical practice remains challenging. It is difficult to identify AKI patients using retrospective data; information preprocessing and the limitations of existing models pose problems. It is essential to embrace standardized labeling criteria and to form international multi-institutional collaborations that foster high-quality data collection. Additionally, existing constraints on the deployment of evolving AI technologies in real-world healthcare settings and enhancement of the reliabilities of AI outputs are crucial. Such efforts will improve the clinical applicability, performance, and reliability of AKI Clinical Support Systems, ultimately enhancing patient prognoses. |
| Keywords:
Artificial intelligence ; Acute kidney injury ; Machine learning ; Clinical decision support systems |
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