**7. Conclusions**

AKI has a significant negative impact on short and long-term outcomes; thus, it is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI. The increasing amount of evidence is encouraging the real-time implementation of these ML risk models as this does not require additional AKI biomarker testing. Combining these risk prediction models with early care bundles in the future is likely to improve patient outcomes.

**Author Contributions:** The authors participated as follows: J.G. drafted the article, T.B. participated in the acquisition of data, J.A.L. revised the article and approved the final version to be submitted for publication. All authors have read and agreed to the published version of the manuscript.

**Funding:** There was no funding for this study.

**Conflicts of Interest:** There is no conflict of interest. The results presented in this paper have not been published previously in whole or part.
