Calibrating Trust in AI for Clinical Dentistry: A Qualitative Study of Human- AI Collaboration and Adoption Dynamics

Artificial intelligence (AI) is increasingly integrated into clinical dentistry, offering enhancements in diagnostic accuracy, workflow efficiency, and decision-making support. However, its adoption remains uneven, largely due to challenges in trust calibration. This involves finding the right balance between overreliance and underuse. This qualitative study investigates how dental professionals perceive, develop, and recalibrate trust in AI tools, drawing on seven in-depth interviews with clinicians across specialties and geographies. Findings reveal three distinct user clusters: Cautious Verifiers, Balanced Validators, and Overreliant New Graduates, each with unique trust behaviors and verification strategies. Key factors influencing trust include interpretability, reliability, workflow integration, and clinician education. The study introduces an empirically grounded adaptation of the Inverted U-Model of AI Utilization, which links optimal performance to moderate verification (20–50% of AI outputs). These insights inform a trust-centered framework for safe and effective AI adoption, with implications for system design, training, and regulatory policy in dentistry and beyond.