Artificial intelligence (AI)
holds a potentially transformational change in the healthcare industry.
Opportunities such as improved diagnostic accuracy, personalized
treatment, and reduced administrative burden have been broadly discussed in the
previous studies. In terms of actual implementation, there is limited research that explains the healthcare decision-makers’
cautious-pace approach to scaling AI technology in healthcare organizations.
The aim of this study is to review the existing literature to explore the key
challenges that justify the slow adoption
rate of artificial intelligence in the healthcare sector. The research also aims at providing a thorough understanding
of challenges that prevent healthcare organizations from harnessing the
benefits of AI. To achieve these goals, a literature review of 324 papers has
been conducted to identify the internal and external key challenges and their
impacts on the adoption of artificial intelligence in the healthcare sector.
The results indicate that expanding the
utilization of artificial intelligence technologies in healthcare has encountered several challenges emerging from technological capabilities, regulations and policies, data management, and the ethical landscape surrounding the use of AI. The
findings of this study contribute to the body of knowledge by exploring
the artificial intelligence adoption challenges.
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