Introduction Early-stage proximal caries detection on bitewing radiographs is challenging and hampered by inter-observer variability. This study aimed to assess how artificial intelligence (AI) support influences radiographic detection and staging of proximal early caries lesions, confidence, and assessment time across observer experience levels. Methods Eighteen observers (dental students, general dentists, and dental radiologists) evaluated 369 proximal surfaces on 25 bitewing radiographs in two sessions: first unaided, then aided with AI-annotated caries lesion regions. Observers assigned lesion stages and provided self-reported (SR) confidence ratings. Radiographic detection and staging accuracy relative to an expert consensus reference standard was assessed using cumulative link mixed models (CLMM). Ordinal agreement and binary detection metrics were computed for each observer group. Results The AI-based tool had divergent effects across the different groups of observers. Dental students showed a statistically significant improvement in radiographic detection and staging accuracy relative to the reference standard (p = 0.039). General dentists and dental radiologists showed no statistically significant change in radiographic detection and staging accuracy when using AI support (p = 0.28 and p = 0.15, respectively). SR-confidence increased when the AI-based tool was used for all groups. Conclusion AI support induced a shift in observers' detections toward the AI-based tool's performance pattern, with its effect on radiographic detection and staging accuracy varying across observer groups and no statistically significant change observed in more experienced observers. AI generally increased the SR-confidence of the clinicians.

Dental caries management requires iterative clinical reasoning, yet treatment thresholds and intervention pathways vary considerably among dentists despite established evidence-based frameworks. This study aims 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 Domains 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.