Overcoming barriers to AI implementation in dentistry: a comprehensive ISM model analysis

Artificial intelligence (AI) in the present scenario stands at the forefront of innovation, offering transformative potential across all domains, including healthcare. Recognizing the need for a deeper understanding of the barriers to AI implementation in dentistry, this study aims to explore and analyse the challenges hindering its widespread adoption.

The study employed a mixed-method approach. First, we conducted a detailed review of past studies to identify possible barriers. About 18 experts helped in finalizing the 17 important barriers. We used Interpretive Structural Modelling (ISM) to understand how these barriers are connected. Further, Matrice d'impacts croisés multiplication appliquée à un classement (MICMAC) analysis was operationalised to group the barriers and assess their effect on other barriers.

The study found several major barriers. These include fear of biasness, technology sophistication, firm size and structure, lack of trained staff, unavailability of data, privacy and security issues. Technological hurdles and lack of accountability are found to be the linkage variables linking the driver and dependent quadrants. ISM helped show how some barriers are more powerful and affect others. MICMAC analysis grouped the barriers into four types, namely independent, dependent, linkage and driving.

Dentists, technology makers and policymakers can use these results to build trust in AI systems and improve dental care.

This is one of the initial attempts to use both ISM and MICMAC methods to study the role of AI in dentistry.