The Economic Impact of AI-Driven Image Classification in Acute Kidney Injury Detection

Guoyuan Zhang^*

• East China University of Political Science and Law, Shanghai, China

Artificial Intelligence (AI) is transforming the healthcare industry, particularly in the early diagnosis and management of critical conditions such as Acute Kidney Injury (AKI). Traditional diagnostic approaches, while clinically validated, often suffer from delays and limited scalability.This study introduces a novel AI-driven Economic Modeling (ADEM) framework that integrates AI-based image classification with real-time economic decision-making to quantify the financial value of automated AKI detection. Our method combines convolutional neural networks and transformer-based models for high-accuracy kidney image classification, which is then linked to adaptive utility learning and dynamic resource pricing modules. Experimental results show that fullscale deployment of ADEM achieves a 2.1× return on investment (ROI) and reduces ICU-related hospitalization costs by up to $89,000 per 1,000 patients. Moreover, targeted deployment for highrisk patient groups improves ROI to 3.2× while reducing ICU admission rates by 23.8%. These findings demonstrate that AI-enabled diagnostics not only enhance early detection accuracy but also offer significant economic benefits. This work provides a critical foundation for integrating AI tools into clinical workflows with an emphasis on sustainability, scalability, and cost-efficiency.