Exploring Drivers And Barriers Of Adopting Ai-Driven Technologies In Health Systems

In light of recent Artificial Intelligence (AI) advancements in healthcare applications, such as medical imaging, disease prediction, precision medicine, and telehealth, there is substantial potential for enhancing and personalizing the experience while ensuring improved operations efficiency. However, the widespread integration of AI in healthcare has given rise to significant concerns, necessitating an in-depth investigation. These concerns can potentially hinder the adoption of AI-driven technologies in health systems, warranting a thorough examination. One critical concern is automation bias, where an unquestioning reliance on AI outputs may compromise pivotal decision-making processes. Other concerns, such as the”black-box” nature of AI algorithms and privacy concerns regarding sharing data between stakeholders, impact the perceived trust of AI technologies and require more robust measures when implementing AI technologies in healthcare. This study employs a systems approach to study the drivers and challenges influencing the adoption of AI-powered technologies in healthcare systems. The proposed model considers different perspectives, such as patient-related factors, physician characteristics, organizational readiness, and governance. The distinct classification of these factors highlights the necessity for a systems approach and a comprehensive strategy that considers the perspectives of all stakeholders in the adoption process. The findings of this study also recommend continuous collaboration among stakeholders and informed decision-making to ensure the safe and efficient integration of AI technologies in healthcare.