Role of artificial intelligence in the diagnosis and management of kidney disease: applications to chronic kidney disease and acute kidney injury.

ABSTRACT

PURPOSE OF REVIEW Chronic kidney disease (CKD) and acute kidney injury (AKI) are global public health problems associated with a significant burden of morbidity, healthcare resource use, and all-cause mortality. This review explores recently published studies that take a machine learning approach to the diagnosis, management, and prognostication in patients with AKI or CKD. RECENT FINDINGS: The release of novel therapeutics for CKD has highlighted the importance of accurately identifying patients at the highest risk of progression. Many models have been constructed with reasonable predictive accuracy but have not been extensively externally validated and peer reviewed. Similarly, machine learning models have been developed for prediction of AKI and have found sufficient accuracy. There are issues to implementing these models, however, with conflicting results with respect to the relationship between prediction of an AKI outcome and improvements in the occurrence of other adverse events, and in some circumstances potential harm. SUMMARY: Artificial intelligence models can help guide management of CKD and AKI, but it is important to ensure that they are broadly applicable and generalizable to various settings and associated with improved clinical decision-making and outcomes.