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

Area of Science:

• Dentistry
• Artificial Intelligence
• Healthcare Innovation

Background:

• Artificial intelligence (AI) offers transformative potential in healthcare.
• Understanding barriers to AI implementation in dentistry is essential for its successful adoption.
• This study addresses the need for a deeper analysis of challenges hindering AI in dental practices.

Purpose of the Study:

• To explore and analyze the barriers hindering the widespread adoption of AI in dentistry.
• To identify and categorize the key challenges impacting AI implementation in the dental field.
• To provide insights for overcoming obstacles to AI integration in dental care.

Main Methods:

• A mixed-method approach was utilized, starting with a comprehensive literature review.
• Interpretive Structural Modelling (ISM) was employed to map the interconnections between identified barriers.
• Matrice d'impacts croisés multiplication appliquée à un classement (MICMAC) analysis was used to classify barriers based on their influence.

Main Results:

• Key barriers identified include fear of bias, technological sophistication, firm size, lack of trained staff, data unavailability, and privacy concerns.
• Technological hurdles and lack of accountability were identified as linkage variables connecting different barrier categories.
• ISM revealed the hierarchical influence of certain barriers, while MICMAC analysis categorized them into independent, dependent, linkage, and driving types.

Conclusions:

• The findings provide actionable insights for dentists, technology developers, and policymakers to foster trust in AI systems.
• Addressing identified barriers is critical for enhancing the integration and effectiveness of AI in dental care.
• This research pioneers the combined use of ISM and MICMAC methodologies to analyze AI adoption challenges in dentistry.