The use of AI in healthcare is rapidly accelerating, with a constant flow of publications and announcements introducing new algorithms and applications aimed at improving patient care, optimizing resource allocation, reducing medical errors, and enhancing patient-centered care models. However, the process of implementing AI technologies in clinical practice remains complex and challenging.
In this paper, the authors provide a comprehensive analysis of the barriers and strategies for the successful implementation of AI in healthcare settings. The study uses a mixed-method design, combining systematic literature reviews with qualitative interviews conducted with healthcare leaders and professionals. It identifies several key barriers to AI adoption, categorized into three phases of implementation: planning, implementing, and sustaining the use of AI systems. The identified barriers and strategies are:
Barriers
- Leadership and change management
- Data quality and access
- Legal and ethical concerns
- Training and workflow integration.
Strategies
- Leadership involvement
- Data management
- Ethical considerations
- Sustained training
- Pilot testing and monitoring
