Machine learning studies of drug-induced nephrotoxicity: a scoping review

Background: Machine learning methods have emerged as a promising approach to prevent drug-induced nephrotoxicity. Objective: This review evaluates the quality and highlights recent advances of machine learning algorithms for predicting drug-induced nephrotoxicity. Eligibility criteria: Studies on machine learning models to predict drug-induced acute kidney injury, acute kidney disease, or both published between January 2014 and August 2024 were eligible. Sources of evidence: A comprehensive search was conducted by using PubMed, Embase, Web of Science, Cochrane Library, and Scopus. Charting methods: A standardized charting form was developed based on CHARMS, TRIPOD+AI, and PROBAST tools to assess the quality and risk of bias across studies. Results: From the initial 5,179 articles searched, 24 studies were included in this review. All studies achieved good area under the receiver operating characteristic curves (AUROCs) above 0.75, with boosting machines being the most frequently outperforming algorithms ( n = 7, 29.17%), and neural networks showed the highest median AUROC of 0.90 (0.86–0.92). Two-thirds of studies ( n = 16; 66.67%) predicted acute kidney injury, whereas only 5 (20.83%) focused on acute kidney disease. Estimated glomerular filtration rate, blood urea nitrogen, serum creatinine, hemoglobin, and albumin emerged as the most utilized features by 10 (41.67%), 9 (37.5%), 9 (37.5%), 8 (33.33%), and 8 (33.33%) studies, respectively. Diabetes, heart failure, diuretics, and non-steroidal anti-inflammatory drugs were frequently selected features by 7 (29.17%), 5 (20.83%), 5 (20.83%), and 4 (16.67%) studies, respectively. The 2025 PROBAST+AI risk-of-bias assessment indicated that 7 (29.17%) studies had a low risk of bias. A high risk of bias was observed in 20 (83.33%), 18 (75%), and 17 (70.83%) studies due to insufficient performance evaluation, small sample sizes, and lack of external validation. Conclusion: Recent machine learning studies have demonstrated great performance using clinically obtainable features. Incorporating acute kidney injury and disease, methodological enhancement, and guideline adherence can facilitate clinical applicability in preventing drug-induced nephrotoxicity.