Artificial intelligence (AI) is increasingly deployed across healthcare systems to support clinical decision-making, optimize operational processes, and improve population health outcomes. Despite substantial investment and rapid advances in model performance, real-world adoption and sustained impact remain limited. A central barrier is the challenge of trust among clinicians, administrators, patients, and regulators in AI-enabled systems. While the concept of “trustworthy AI” is widely invoked, existing frameworks largely emphasize technical model properties and ethical principles without sufficient guidance for operational implementation. This paper argues that trustworthiness is not an intrinsic attribute of AI models but an emergent property of socio-technical systems in which AI is embedded. We propose a comprehensive framework for operationalizing trustworthy AI that shifts attention from model-centric validation to workflow-level design, governance, and continuous evaluation. By integrating decision-centered design, human–AI role delineation, failure visibility, embedded accountability, and longitudinal performance monitoring, the framework provides a pragmatic foundation for deploying AI systems that are safe, equitable, and sustainable in both clinical and operational contexts.
Kunal Khashu (2026) studied this question.