Interdisciplinary Collaboration in AI-Driven Nursing: Current Developments, Challenges, and Future Perspectives

Abstract

Artificial intelligence (AI) is increasingly integrated into nursing, offering opportunities for clinical decision support, risk detection, personalized care, workflow optimization, and management, while raising concerns about clinical judgment, professional agency, accountability, data governance, and patient safety. This paper examines interdisciplinary collaboration in responsible AI integration, focusing on the relationship between AI outputs and professional nursing judgment. It addresses three issues: AI’s influence on nurses’ clinical reasoning and the need to preserve professional judgment; nurses’ agency as clinical co-designers, evaluators, and contributors across the AI lifecycle; and ethical/governance challenges, including accountability, privacy, data governance, algorithmic bias, validation, and oversight. The paper proposes bounded human–AI collaboration and bounded delegation, whereby AI augments selected nursing tasks when its capabilities, limitations, risks, and responsibilities are clearly defined, while professional judgment and human oversight remain central. Interdisciplinary collaboration should extend to validation, implementation, monitoring, and evaluation. Nurses need adequate AI literacy and agency to appraise outputs, identify discrepancies, and improve systems. Responsible integration requires sustained collaboration, meaningful nursing participation, robust governance, and continuous oversight, positioning nurses as active partners to advance patient safety, effectiveness, equity, and person-centered care.