Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.
Masooma HassanAndre W KushnirukElizabeth M BoryckiPublished in: JMIR human factors (2024)
The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.
Keyphrases
- artificial intelligence
- healthcare
- machine learning
- deep learning
- big data
- primary care
- clinical practice
- end stage renal disease
- electronic health record
- health information
- quality improvement
- ejection fraction
- newly diagnosed
- chronic kidney disease
- single cell
- highly efficient
- ionic liquid
- room temperature
- social media
- metal organic framework
- affordable care act