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