Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study

Area of Science:

• Dental diagnostics
• Artificial Intelligence (AI)
• Health informatics

Background:

• The integration of AI in healthcare is rapidly evolving.
• Dental diagnostics, particularly radiography, presents opportunities for AI enhancement.
• Understanding implementation challenges is crucial for AI adoption in dentistry.

Purpose of the Study:

• To identify barriers and enablers for AI implementation in dental radiographic diagnostics.
• To explore stakeholder perspectives (dentists and patients) on AI in dental diagnostics.
• To inform strategies for fostering AI adoption in dental practices.

Main Methods:

• Semi-structured phone interviews conducted with dentists and patients.
• Utilized a questionnaire based on the Theoretical Domains Framework (TDF) and COM-B model.
• Employed Mayring's content analysis to identify themes.

Main Results:

• Identified 36 barriers and enablers across nine TDF domains and three COM-B determinants.
• Stakeholders expressed optimism regarding AI's potential in dental diagnostics.
• Key enablers include enhanced diagnostic accuracy, reduced workload, and improved communication.
• Significant barriers involve AI reliance, decision-making responsibility, and AI explainability.

Conclusions:

• AI offers substantial benefits for dental diagnostics, including improved accuracy and efficiency.
• Addressing concerns about AI reliance, accountability, and transparency is vital for successful implementation.
• Recommendations are provided for decision-makers and industry to facilitate AI integration in dentistry.