Artificial Intelligence in Healthcare Sector: A Literature Review of the Adoption Challenges

Abstract

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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