Acute kidney injury prediction and prognostication using machine learning

Acute Kidney Injury (AKI) is a major health concern with high costs and poor outcomes, partly due to late diagnosis. This paper reviews the application of artificial intelligence (AI) and machine learning (ML) to overcome these limitations by enabling earlier and more accurate prediction and prognostication of AKI. Many studies on AI and ML models used to predict and prognosticate AKI were included in the review. The focus was on models that analyze complex datasets, including real-time data streams like novel biomarkers and continuous vital signs, to achieve earlier and more accurate predictions than conventional methods. ML models show high predictive accuracy for AKI onset and outcomes across various clinical settings, including intensive care units, sepsis, and postoperative and postcontrast situations, with key findings like the successful integration of real-time data to reflect the evolving nature of kidney injury. AI and ML offer a powerful, proactive solution for AKI management. By leveraging diverse data, these technologies can significantly improve patient outcomes and reduce healthcare costs. While their promising performance warrants further exploration, successful clinical integration will require user-friendly platforms and continued validation.