Dental Provider Experiences with AI Radiograph Annotation: A Qualitative Case Study

Area of Science:

• Oral health informatics
• Dental diagnostics
• Artificial intelligence in healthcare

Background:

• Artificial intelligence (AI) tools are increasingly used in dentistry for diagnosis, treatment planning, and education.
• Radiographic annotation software, a type of AI, highlights potential oral disease indicators on dental radiographs.

Purpose of the Study:

• To explore the challenges and facilitators influencing the adoption and use of AI radiograph annotation software among oral health professionals.
• To understand the experiences of dental providers with AI annotation software within a large US healthcare organization.

Main Methods:

• A qualitative collective case study design was employed.
• Semi-structured interviews were conducted online with dentists, dental hygienists, and dental therapists across five dental clinics.
• Abductive coding was used to identify themes related to providers' experiences with the AI software.

Main Results:

• Key themes included initial impressions, evolving beliefs, utilization patterns, diagnostic confidence, treatment plan alignment, and patient-centered care considerations.
• Providers recognized the AI software as a decision-support tool, not a replacement for clinical judgment.
• Unexpected benefits and challenges associated with the AI annotation software were identified.

Conclusions:

• Findings offer insights for healthcare organizations on supporting the implementation of AI software protocols for oral health professionals.
• Effective AI integration necessitates structured onboarding, continuous training, and peer support to optimize its value.
• Adoption of AI tools can enhance diagnostic confidence, patient engagement, and early disease detection, potentially improving oral health outcomes.