Artificial Intelligence–Enabled Lactate Monitoring and Metabolic Biomarker Integration for Early Diagnosis of Acute Kidney Injury

Acute kidney injury (AKI) is a prevalent clinical syndrome with high mortality, yet early diagnosis remains challenging due to the delayed response and limited sensitivity of conventional indicators. This review uniquely integrates lactate as a dynamic, time‐varying feature within artificial intelligence (AI) frameworks in the early diagnosis of AKI. We summarize recent advances in AI architectures, from gradient boosting to multimodal deep learning, that leverage lactate, alone or combined with biomarkers such as neutrophil gelatinase‐associated lipocalin (NGAL), for early AKI detection and risk stratification. We critically discuss the persistent gap between retrospective model performance and prospective clinical validation. By bridging data‐driven insights with renal metabolic reprogramming biology, AI‐enabled lactate monitoring offers a promising pathway toward personalized AKI management. This framework provides a roadmap for researchers developing next‐generation prediction tools and clinicians seeking to integrate real‐time lactate monitoring into practice, highlighting the need for standardized data and robust prospective validation.