Related Experiment Video
Updated: Jan 15, 2026

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
Using Predictive Models and AI for AKI Research
Madhumitha Rajagopal1, Lili Chan2, Girish N Nadkarni3
1Samuel Bronfman Department of Medicine, The Barbara T Murphy Division of Nephrology Icahn School of Medicine at Mount Sinai, New York, NY.
Abstract:
Acute kidney injury (AKI), a drop in kidney function with multiple etiologies, is a common complication in hospitalized patients and is associated with poorer patient outcomes. With the advent of electronic health records, machine learning algorithms have been developed that can predict the incidence and severity of AKI, AKI persistence, as well as patient outcomes like mortality and the need for kidney replacement therapies. Furthermore, it can risk-stratify patients based on early presentations to aid with clinical management. Newer technologies like natural language processing and generative artificial intelligence (AI) (e.g., ChatGPT) also show promise in the realm of AKI prediction and management. This review provides an overview of the role of AI in adults with AKI, as well as explores some limitations and ethical considerations that need to be addressed as we move forward.
Related Concept Videos
Acute Kidney Injury IV: Diagnostic Studies and Prevention
Acute Kidney Injury I: Introduction
Acute Kidney Injury II: Pathophysiology
Acute Kidney Injury V: Interprofessional Care
Acute Kidney Injury VI: Nursing Management
Acute Kidney Injury III: Clinical Manifestations

