The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising key advancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency. Using an in-depth analysis of the extensive amount of research on artificial intelligence and how it could help the medical industry, this study identified eleven barriers and challenges. Interpretive structural modeling (ISM) was used as a methodological approach to determine the relationship between the difficulties extracted and their dependency and driving powers. It resulted in a five-tiered model, with the introduction of innovative and new-generation tools topping the chart as the most dependent challenge. Similarly, Insufficient Data, Data Acquisition, Data Misuse, and Missing Compassion were the key drivers. Therefore, during the implementation of artificial intelligence in medicine, these challenges should be considered. Although artificial intelligence (AI) possesses the groundbreaking power to enhance patient care and operational efficiency in the healthcare sector, there are several key problems that must be addressed for implementation to be fruitful. The order of these challenges was ascertained through interpretive structural modeling, underlining the significance of innovation and data-related issues. Health systems can optimize AI’s benefits and enhance diagnosis, patient care, and overall hospital management by aggressively eliminating its deterrents.

The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising keyadvancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency.The implementation of artificial intelligence into the healthcare system has been very transformational, productive, and hopeful, with the potential to revolutionize diagnosis, treatment, patient care, administrative work, and operational efficiency. Using an in-depth analysis of the extensive amount of literature on artificial intelligence and how it could help the medical industry, this study identified the eleven barriers and challenges. An interpretative structural modelling (ISM) has been used as a methodological approach to find out the relationship between the extracted challenges and their dependency and driving powers. It resulted in a five-tiered model with introduction of innovative and new generation tools topping the chart as the most dependent challenge. Similarly Insufficient Data, Data Acquisition, Data Misuse and Missing Compassion being the key drivers. Thereby, during the implementation of artificial intelligence in medicine, there challenges should be considered.