Cross-Domain Responsible Artificial Intelligence: From Healthcare and Radiology to Education Analytics

Abstract

Artificial intelligence systems increasingly operate across domains where errors, bias, or opacity carry significant human and institutional consequences. Healthcare, radiology, and education analytics represent particularly sensitive environments in which automated decisions influence diagnosis, treatment planning, learning outcomes, and long term social trajectories. While advances in machine learning have enabled impressive predictive accuracy, concerns around trust, explainability, data quality, and governance remain unevenly addressed across application areas. This study investigates responsible artificial intelligence from a cross-domain perspective, examining how principles and practices developed in healthcare and radiology can inform more accountable and trustworthy education analytics systems. Through a unified analytical framework and empirical evaluation across representative datasets, the work demonstrates that responsible AI is not domain specific but emerges from consistent attention to transparency, validation, fairness, and human oversight. The findings highlight transferable design patterns and evaluation strategies that support safe and effective AI adoption across high impact sectors.

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