How Artificial Intelligence Reshapes Clinical Decision Thresholds in Cariology: A Qualitative Study of Dentists

Dental caries management requires iterative clinical reasoning, yet treatment thresholds and intervention pathways vary considerably among dentists despite established evidence-based frameworks. This study aimed to examine how dentists integrate AI-supported diagnostic outputs into clinical reasoning in cariology, and how socio-technical conditions shape this process.

Semi-structured interviews were conducted with 20 dentists from diverse specialties and practice settings in Türkiye. Interviews were developed and analyzed using the COM-B model and theoretical domain framework to identify capability-, opportunity-, and motivation-related determinants of AI adoption.

Reflexive thematic analysis revealed three interrelated patterns: (1) AI as a cognitive partner enhancing diagnostic confidence and preventive orientation; (2) structural and economic barriers limiting equitable access and shaping reliance on algorithmic outputs; and (3) ethical and professional concerns, including trust, liability, and surveillance, influencing the extent to which AI recommendations are accepted or resisted.

Rather than directly shifting diagnostic thresholds, AI integration appears to reconfigure the socio-technical conditions under which clinical judgments are formed. Understanding these contextual determinants is essential for responsible and equitable AI implementation in cariology.