As AI-driven tools become increasingly embedded in clinical workflows, healthcare professionals must understand not only how these systems function, but how to use them responsibly in real-world care settings. This session provides a practical, foundational overview of Responsible AI tailored for clinical informaticists, clinical leaders, and professionals with direct nursing practice roles. Participants will conceptually define Responsible AI, exploring core principles such as fairness, transparency, privacy, accountability, and trust—particularly as they apply to clinical decision support and workflow-integrated AI. The session will examine the current regulatory landscape shaping AI use in healthcare, highlighting emerging expectations around safety validation, oversight, and clinical responsibility without requiring deep regulatory expertise. The session we will explore strategies for integrating AI—including predictive and generative models—into clinical nursing workflows in ways that support care delivery, documentation, and decision-making while minimizing cognitive burden and workflow disruption. Finally, the session will review lessons learned from AI implementations at scale, offering practical insights into what works, what doesn’t, and how clinical informatics professionals can proactively address risk, trust, and accountability as AI adoption continues to accelerate.

Learning Objectives

• Explain Responsible AI principles and their practical relevance to clinical informatics and direct care roles
• Describe the current regulatory landscape shaping AI use in healthcare, including emerging expectations for oversight and accountability
• Evaluate strategies for integrating AI tools, including predictive and generative models, into clinical nursing workflows while minimizing cognitive and documentation burden
• Understand lessons learned from real-world AI implementations to proactively address risk, trust, and governance challenges in clinical settings