Artificial Intelligence in Gingival Recession Assessment and Treatment Planning: A Scoping Review

Background/Objectives: Artificial intelligence (AI) and machine learning (ML) are increasingly used in periodontal assessment, image analysis, outcome prediction, and clinical decision support. However, evidence specifically addressing gingival recession (GR) and related periodontal soft-tissue parameters remains fragmented and predominantly technical. This scoping review aimed to map AI/ML applications for the detection, measurement, prediction, and treatment planning of GR and to identify methodological limitations and translational evidence gaps. Methods: This scoping review was conducted in accordance with the JBI methodology for scoping reviews and reported according to PRISMA-ScR. Eligibility was structured according to the Population–Concept–Context framework and included English-language original studies involving human participants or human-derived clinical data and evaluating AI/ML approaches directly related to GR or to periodontal soft-tissue or anatomical parameters relevant to its assessment or management. PubMed/MEDLINE, Scopus, and Embase were utilized as the core bibliographic databases, while Google Scholar was used as an additional search source with screening restricted to the first 200 results ranked by relevance. No restrictions on publication year or start date were applied. Study characteristics, AI methods, clinical applications, validation strategies, and performance metrics were charted and descriptively synthesized. Results: Twelve studies were included across four main clinical domains: detection and segmentation of gingival recession and related soft-tissue features, measurement and anatomical landmark localization, prediction and risk assessment, and treatment planning or clinical decision support. AI/ML models generally demonstrated promising technical performance, including high classification and segmentation accuracy and submillimeter localization of periodontal anatomical landmarks. However, datasets, reference standards, validation strategies, and reported performance metrics were heterogeneous. Independent external validation remained limited, and most studies assessed technical performance rather than clinical utility. No study prospectively demonstrated that AI-assisted assessment or decision support improved clinician decision-making or patient outcomes. Conclusions: AI shows potential to support objective GR assessment, prediction, and treatment planning in gingival recession management. However, current evidence remains predominantly proof-of-concept. Multicenter external validation and prospective evaluation in real-world clinical workflows are required before routine implementation.