Implementing Ethical AI Across Cyber-Physical Systems and Diagnostic Contexts: Transparency, Contestability, and Trust

Abstract

The integration of artificial intelligence into healthcare systems presents multifaceted technical and pedagogical challenges that require comprehensive multidisciplinary approaches. This article examines the practical obstacles encountered in AI implementation, encompassing technical issues such as data quality, interoperability, and regulatory compliance, alongside pedagogical challenges related to workforce training, professional development, and educational integration. Through systematic analysis of contemporary literature and case evidence, the study identifies critical challenges including data cleansing complexities, predictive variable inconsistency, instructor competency requirements, and interdisciplinary knowledge gaps. Findings reveal that successful AI integration demands collaboration across computer science, clinical practice, law, and education, with particular attention to data privacy, security, and ethical compliance. The article proposes a multidisciplinary framework for addressing technical and pedagogical challenges, emphasizing the importance of continuous refinement, interdisciplinary expertise, and adaptive educational strategies. The GDPR framework provides essential regulatory parameters that must be integrated with technical and pedagogical approaches to ensure responsible AI implementation in healthcare.