Systematic Review of Artificial Intelligence in Pre-Implant Radiographic Imaging: A Structured Qualitative Synthesis of Performance Across Imaging Modalities and AI Architectures.

Authors

M Jeevitha, Pachipulusu Balaji, B T Kavya, K S Rajesh, G S Keerthana

Published in

The International journal of oral & maxillofacial implants. Volume 0. Issue 0. Pages 1-25. Oct 19, 2026. Epub Oct 19, 2026.

Abstract

To evaluate contemporary evidence on artificial intelligence (AI) applied to radiographic imaging for pre-implant dental assessment and treatment planning, with emphasis on the distinction between technical algorithmic performance and clinically oriented planning outcomes.
Peer-reviewed original studies published from January 2021 through December 2025 were identified through PubMed/MEDLINE, the Cochrane Library, ScienceDirect, JSTOR, and supplementary searches. Eligible studies used radiographic imaging as an integral input to AI for pre-implant assessment, planning, decision support, virtual positioning, or outcome prediction. Reporting completeness was assessed with CLAIM 2024. Risk of bias was assessed with QUADAS-3 for eligible diagnostic/test-accuracy studies, PROBAST+AI for the prognostic prediction-model study, and RoB 2 for the randomized clinical study. Owing to heterogeneity, findings were synthesized qualitatively.
Thirty-six studies were included. CBCT was the predominant modality. Applications included anatomical detection and segmentation, bone assessment, decision support, implant-position prediction, automated virtual placement, workflow integration, and preoperative outcome prediction. Segmentation and anatomical-analysis studies generally reported high task-specific performance, while clinically oriented positional accuracy and planning outcomes were more variable. AI frequently reduced workflow time. QUADAS-3 was applicable to five studies; four had high overall risk of bias and one unclear risk. The prognostic prediction-model and randomized clinical studies also had high risk of bias under their respective tools.
AI shows greatest current support for anatomical analysis and selected workflow tasks. Evidence for autonomous implant positioning and outcome prediction remains limited and heterogeneous. Current findings support clinician-supervised integration, with further multicenter external testing, standardized clinically meaningful outcomes, reproducibility, and prospective evaluation required before broader routine implementation.

PMID:
42896035
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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