The machine-learning-based AKIpredictor achieved similar discriminative performance as physicians for prediction of AKI-23, and higher net benefit overall, because physicians overestimated the risk of AKI.

This suggests an added value of the systematic risk stratification by the AKIpredictor to physicians’ predictions, in particular to select high-risk patients or reduce false positives in studies evaluating new and potentially harmful therapies.

Due to the low event rate, future studies are needed to validate these findings.

252 patients were included, 30 developed AKI-23. In the cohort of patients with predictions by physicians and AKIpredictor, the performance of physicians and AKIpredictor were respectively upon ICU admission.

Prospective observational study in five ICUs of a tertiary academic center. Critically ill adults without end-stage renal disease or AKI upon admission were considered for enrollment.

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