Oral implanomics: transitioning from experience-based discipline to AI-driven therapeutic paradigm - radiomics and artificial intelligence in oral implantology.

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Abstract

Oral implantology has experienced substantial digital transformation over the past two decades. The adoption of cone-beam computed tomography (CBCT), computer-assisted surgery, and artificial intelligence (AI) has improved diagnostic accuracy, treatment planning, and procedural precision. However, clinical decision-making remains largely dependent on conventional radiographic interpretation and clinician experience. We propose the concept of Implanomics (Implant-Omics) as an integrative framework combining radiomics, AI, and digital workflows. Radiomics, which converts medical images into quantitative data, offers new opportunities to extract imaging biomarkers from routine CBCT examinations. By characterizing bone morphology, trabecular architecture, and image texture beyond visual assessment, radiomics may support more objective evaluation of implant sites and improve prediction of treatment outcomes. When combined with machine-learning algorithms, these imaging-derived features can be incorporated into predictive models for risk assessment, implant planning, and longitudinal monitoring. In this Perspective, we discuss the evolution of implant dentistry from experience-based planning toward imaging-driven decision support and examine the emerging role of radiomics and AI in precision implant care. We highlight current applications, key challenges, and future directions for integrating quantitative imaging analysis into clinical workflows. Although substantial validation and implementation challenges remain, the convergence of radiomics, artificial intelligence, and digital implant technologies may contribute to more individualized and evidence-informed treatment strategies in implant dentistry.

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