Transforming Ocular Toxicity Assessment Through AI: A White Paper on the ICE Test Analysis Tool

Abstract

Ocular toxicity assessment is a critical component of safety evaluation for pharmaceuticals, chemicals, and consumer products. The Isolated Chicken Eye (ICE) test is a widely accepted alternative method for identifying severe eye irritants, offering ethical and scientific advantages over traditional in vivo testing. Recent advances in artificial intelligence (AI) have opened new opportunities to enhance the accuracy, consistency, and efficiency of ICE test analysis. This white paper explores the integration of AI-driven analytical frameworks into ocular toxicity assessment, focusing on automated data interpretation, image-based scoring, and predictive modeling. By reducing subjectivity and improving reproducibility, AI-supported ICE test analysis has the potential to strengthen decision-making, accelerate safety evaluations, and support regulatory acceptance. The convergence of AI and alternative toxicity testing represents a transformative step toward more reliable, ethical, and data-driven ocular safety assessment.