A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology

作者：Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang · 发表于：Renal Failure · 年份：2025 · DOI：10.1080/0886022x.2025.2513002 · 被引用次数：17 · 研究领域：Artificial Intelligence in Healthcare and Education、Renal and Vascular Pathologies、Renal cell carcinoma treatment

Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). AI-driven models utilize diverse datasets-including electronic health records, imaging, and biomarkers-to improve clinical decision-making. Applications such as convolutional neural networks for kidney biopsy interpretation, and predictive modeling for renal replacement therapies underscore AI's potential. Nonetheless, challenges including data quality, limited external validation, algorithmic bias, and poor interpretability constrain the clinical reliability of AI/ML models. To address these issues, this article offers a structured framework for nephrologist peer reviewers, integrating the TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-AI Extension) checklist. Key evaluation criteria include dataset integrity, feature selection, model validation, reporting transparency, ethics, and real-world applicability. This framework promotes rigorous peer review and enhances the reproducibility, clinical relevance, and fairness of AI research in nephrology. Moreover, AI/ML studies must confront biases-data, selection, and algorithmic-that adversely affect model performance. Mitigation strategies such as data diversification, multi-center validation, and fairness-aware algorithms are essential. Ove...