Artificial Intelligence and Precision Periodontics: From Automated Bone Loss Detection to Patient Specific Care

Abstract Artificial intelligence (AI) is expanding periodontal care from isolated image interpretation toward integrated, patient specific decision support. Current applications include automated detection and quantification of alveolar bone loss, periodontitis classification, prediction of treatment response, assistance with treatment planning, remote behavioural monitoring and measurement of peri implant bone changes. The field has advanced rapidly, and 2025-2026 studies increasingly examine multimodal models that combine radiographs, intraoral scans and clinical records. Nevertheless, most evidence remains retrospective, single-centre and dependent on heterogeneous definitions and reference standards. High accuracy in a selected dataset does not establish clinical readiness, generalizability or benefit to patients. This narrative review explains the principal AI methods used in periodontics, evaluates their roles across diagnosis, prognosis, therapy and supportive care, and proposes a practical pathway for responsible implementation. The most defensible current use of AI is augmentation: standardizing repetitive measurements, organizing complex information, displaying uncertainty and supporting communication while the periodontist retains responsibility for examination, diagnosis and shared decision-making. Future progress requires representative multicentre datasets, external and prospective validation, calibrated predictions, transparent reporting, privacy protection, subgroup evaluation, interoperable records and evidence that AI-assisted workflows improve periodontal outcomes without widening inequality. When governed appropriately, AI may become an important component of precision periodontics, but it should complement rather than replace clinical expertise.