Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

This article reviews the current applications and potential of artificial intelligence (AI), particularly machine learning (ML), in urinalysis, encompassing automated urine test strip and sediment analysis, urinary tract infection (UTI) screening, and interpretation of complex urinary biochemical signatures via mass spectrometry and molecular diagnostics. Retrospective studies demonstrate promising diagnostic performance of AI models, such as extreme gradient boosting (XGBoost) for estimating glomerular filtration rate and random forest classifiers for steroid profiling and cancer detection, yet limitations include small sample sizes, lack of prospective validation, and challenges in model explainability and integration into clinical workflows. The review emphasizes the need for large-scale prospective studies and external validations to assess clinical utility, cost-effectiveness, and generalizability before AI can be routinely implemented in clinical laboratory practice. Additionally, it highlights ongoing developments in smartphone-based point-of-care urinalysis and the importance of multidisciplinary guidelines to ensure responsible and effective use of AI in laboratory medicine.