# A Structural Analysis of AI Implementation Challenges in Healthcare
## 4 Results and Discussion  
We found eleven significant obstacles, namely Insufficient Data (ID), Social Issues (SI), Clinical Implementation (CI), High Cost (HC), Black-Box Scenario (BS), Data Acquisition (DA), Introduction of Innovative and New-Generation Tools (IIN), Missing Comparsion (MC), Data Misuse (DM), Data Privacy and Security (DPS), and Technology Development (TD), in our study of the successful application of AI in healthcare during our investigation. Our research explores the interconnectedness of these issues, even though previous researchers like [PERSON] et al. [4] and [PERSON] [37] have recognized some barriers, such as data constraints, privacy concerns, ethical considerations, and development hurdles. The potential advantages of AI are well highlighted in the literature, including the development of individualized health programs, addressed by [PERSON] [3] and [PERSON] et al. [5], improved decision-making in healthcare management [4], and quicker and more accurate disease detection through comprehensive medical data analysis [5,38]. There is still a lot to learn about the interactions and combined effects of these obstacles on the healthcare  
Figure 3: Micmac analysis. system and how the medical community perceives them, as analyzed and researched by [PERSON] et al. [39]. Our paper sets itself apart by going beyond a simple list of difficulties. We explored the complex interrelationships among these elements, clarifying how they impact one another and mold the short- and long-term uses of AI in healthcare. This research offers a more thorough and nuanced viewpoint, highlighting several ways these difficulties function in concert and providing a stronger foundation for negotiating the hindrances of implementing AI in this crucial industry. In our study, ISM was adopted to construct and analyze these limitations. Insufficient Data (IN), Data Acquisition (DA), Data Misuse (DM), and Missing Compassion (MC) were the independent driving factors. The introduction of innovative and new-generation tools (IIN) topped the chart by being the most dependent and driven factor among the ones under study. This study revealed five levels of hindrances from the pre-identified ones. Level-five obstacles included Insufficient Data (IN), Data Acquisition (DA), and Data Misuse (DM). This implies that the foundational issues in creating an artificial intelligence-integrated healthcare sector system include data availability and collection. It also includes Missing Compassion (MC) as one of the factors that cause the reduction in human emotions with one another when AI is introduced. Next, in the fourth tier, there are Data Privacy and Security (DPS), Black-Box Scenario (BS), and Social Issues (SI). Data Privacy and Security (DPS) and Black-Box Scenario (BS) pose a huge threat, which [PERSON] [9] and [PERSON] et al. [2] also addressed in their studies, as they could lead to the disruption of the entire healthcare system by challenging its authenticity. Social Issues (SI), on the other hand, not only create hindrances in terms of emotional understanding but also disable the ability of understanding and comprehending the pains and troubles of an ailing patient. The Clinical Implementation (CI) challenge follows in tier three, wherein the installation of the complete automation system in practice when using AI in the medical field is very different from the theory. With all the infrastructural changes, software requirements, and data necessities, shifting from our current modes of working and usage to new automations is a daunting task, as analyzed by [PERSON] et al. [4] as well. Moreover, for doctors and other the healthcare sector workers, trusting the diagnosis and results of a system-generated response in matters of life and death can also be very difficult. In tier two, the challenges recorded are Technological Development (TD) and High Cost (HC). While the development progress of technology is rapidly increasing and easing things, it creates hurdles because of users.