Honored and humbled to have our work featured in ASN In The Loop today! 🙏🗞️ Congratulations @rupeshrainamd @md_rupesh academic.oup.com/ckj/article… Our systematic review and meta-analysis, "Artificial Intelligence for Predicting Pediatric Acute Kidney Injury," published in the Clinical Kidney Journal, evaluated 14 AI/ML models across 11 studies (encompassing 33,949 children) for the early prediction of pediatric AKI. 💡 Key Findings: 🔹 Top Performer (Discrimination): Gradient boosting achieved the highest pooled AUC (0.873). 🔹 Top Performer (Accuracy): Random forest demonstrated the highest median sensitivity (0.821) and specificity (0.942). 🔹 The Big Picture: While the data strongly highlights the incredible promise of AI in pediatric nephrology, it also underscores a critical next step: we must prioritize consistent reporting and rigorous external validation to safely bring these tools to the bedside. A huge thank you to my exceptional co-authors, mentors, and the Clinical Kidney Journal and ASN communities for this wonderful recognition. I am so excited to keep pushing the boundaries of what is possible with AI in kidney care! 🧠🫘 Wisit Cheungpasitporn, MD, FACP, FASN, FAST Clinician-Scientist & Professor of Medicine, Mayo Clinic #PediatricNephrology #AKI #AIinMedicine #MachineLearning #KidneyCare #DigitalHealth #ASNInTheLoop #FutureOfMedicine

And on being featured in @ASNKidney 's #ASNintheLoop newsletter!