Leadership for Responsible Artificial Intelligence Adoption in Patient Care: Workforce Redesign and Adaptive Capacity in Health Services

Health services are increasingly confronted with the rapid integration of artificial intelligence into patient care, creating new opportunities and challenges for clinical decision-making, workforce organization, professional roles, and care quality. While AI-based technologies may support diagnosis, monitoring, workflow optimization, risk prediction, and personalized care, their implementation depends not only on technical performance but also on leadership, organizational readiness, professional engagement, ethical governance, and adaptive capacity. Current debates on technology adoption, innovation management, change management, and implementation science highlight that digital transformation in health services is a complex social and organizational process. However, there remains limited understanding of how health leaders can guide responsible AI adoption while redesigning work processes, supporting professionals, safeguarding patient-centered care, and promoting organizational learning. This Research Topic therefore focuses on leadership as a key mechanism for aligning AI adoption with workforce transformation and adaptive capacity in health services.

This Research Topic aims to advance understanding of how health leadership can support the responsible adoption of artificial intelligence in patient care, focusing on workforce redesign and adaptive capacity in health services. The increasing use of AI-based technologies in clinical decision-making, patient monitoring, workflow optimization, risk prediction, and care coordination creates important opportunities, but also exposes organizational, ethical, professional, and managerial challenges. These include unclear professional roles, resistance to change, gaps in digital competencies, risks to professional well-being, concerns about trust and transparency, and the need for appropriate AI governance.

This article collection seeks to bring together studies that examine AI adoption as a sociotechnical and organizational transformation rather than a purely technological process. It aims to explore how leadership can guide change management, support task shifting and new professional competencies, promote organizational learning, strengthen data-driven decision-making, and ensure that AI implementation remains safe, ethical, transparent, and patient-centered. Contributions are welcome on leadership for the responsible adoption of artificial intelligence in patient care, with emphasis on workforce redesign and adaptive capacity in health services.

Specific themes may include:
• AI governance in clinical settings
• Leadership for technology adoption and change management
• Redesign of professional roles, workflows, and competencies
• Task shifting and human–AI collaboration
• Professional engagement, trust, and well-being
• Organizational readiness and learning
• Data-driven leadership and decision-making
• Ethical, transparent, and patient-centered AI implementation
• Strategies to evaluate the impact of AI adoption on care quality, safety, efficiency, and workforce sustainability

This Research Topic welcomes empirical studies, systematic and scoping reviews, conceptual papers, implementation studies, policy analyses, comparative studies, and case studies. Priority will be given to manuscripts with clear theoretical grounding, robust methodological approaches, and practical implications for leadership, management, workforce transformation, and responsible innovation in health services.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Health leadership, Digital transformation, Adaptive capacity, Organizational learning, Artificial intelligence in healthcare, Responsible AI adoption, Patient care, Workforce redesign, Professional well-being, Data-driven leadership, Task shifting, AI governance, Technology adoption, Change management, Implementation science

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.