Conceptual and Governance Gaps in Nursing Regulatory Guidance

Abstract

As Artificial Intelligence (AI) systems become embedded in healthcare delivery, nursing regulatory bodies face unprecedented challenges in developing guidance that protects public safety while enabling appropriate technology use. Our analysis examines a recently published guideline, identifying definitional inadequacies, situating AI within an outdated, anthropocentric paradigm that diverges substantially from contemporary AI scholarship and fails to capture how modern systems actually function. Our analysis also identifies well as three critical gaps between regulatory frameworks and contemporary understanding of AI systems. First, its privacy considerations treat AI as conventional healthcare technology, missing the AI-specific characteristics which are distinct from more traditional healthcare risks. Second, the organization inappropriately transfers complex technical assessment responsibilities from organizations to individual practitioners. Third, while the organization acknowledges algorithmic bias affecting equity-deserving populations, the guidelines do not acknowledge the operational frameworks which are required for bias detection and remediation, again placing technical assessment burdens on individual nurses. To effectively address the implications of AI with regards to nursing regulation, interdisciplinary collaboration, clear organizational accountability frameworks, and evidence-based guidance reflecting technical realities are required. This analysis identifies priority areas for nursing regulatory bodies as they develop or revise AI practice guidance.

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