The matrix in precision dentistry: Quantifying research priorities via scientometrics and expert evaluations

A critical disconnect often exists between academic output and actual clinical use. To bridge this gap, this study proposes and validates a novel scientometric-importance-feasibility matrix (IFM) framework. The authors applied this framework to precision dentistry from January 2016 through October 2025 to show how it can be used in prioritizing research frontiers within advanced settings.

The authors used a dual-validated mixed-methods design. First, a scientometric analysis of 912 articles mapped the global knowledge structure. These academic hot spots were surveyed using best-worst scaling and an IFM, quantifying their priorities from the perspective of expert clinicians.

The analysis uncovered 13 research clusters, highlighting a 3-stage field evolution from phenotype correlation toward the convergence of artificial intelligence and noninterventional techniques. Projecting these onto the IFM revealed distinct priority tiers: machine learning and decision support systems emerged as immediate, highly feasible priorities (tier 1), whereas foundational biological research remained highly important but constrained by clinical translational bottlenecks (tier 2).

This study provides a specific reference framework for precision dentistry and establishes, to the authors' knowledge, the first scientometric-IFM framework as an exploratory approach for evidence-based research prioritization.

This innovative hybrid framework may serve as a potential exploratory framework for quantifying priorities in other medical domains. For precision dentistry, stakeholders may consider prioritizing high-feasibility objects (tier 1) for immediate efficiency while securing long-term funding to overcome foundational biological barriers (tier 2). Practitioners must act as distributed clinical nodes to generate real-world data driving this integrated predictive, preventive, personalized, and participatory medicine model.