Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Implementation Science

Background:

• Artificial intelligence (AI) applications are widespread, yet healthcare implementation remains limited.
• Existing AI implementations in clinical settings offer insights into adoption challenges.
• A theoretical framework is needed to systematically analyze AI implementation in healthcare.

Purpose of the Study:

• To conduct a scoping review of empirical data on barriers and facilitators of AI implementation in clinical settings.
• To analyze implementation factors using the Consolidated Framework for Implementation Research (CFIR).
• To identify key challenges and success factors for AI adoption in healthcare.

Main Methods:

• Scoping review of scientific publications from 2015-2021 on AI in clinical settings.
• Theoretical approach using the Consolidated Framework for Implementation Research (CFIR).
• Deductive and inductive extraction of facilitators and barriers from 19 included studies.

Main Results:

• Facilitators often related to implementation process engagement and management.
• Barriers frequently concerned AI generalizability, system interoperability, and data quality/availability.
• Imbalances in reporting across CFIR domains were noted in the included studies.

Conclusions:

• Healthcare AI implementation requires robust engagement and management strategies.
• Addressing AI generalizability, interoperability, and data issues is crucial for successful adoption.
• Increased implementation science expertise and standardized reporting are needed for AI projects in healthcare.