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TRUVACE RECORD VERSION
record: TRV-2026-1939
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-19T22:02:27.697692Z
status: published
lens: trace
sector: health
headline: Systematic Review of Artificial Intelligence in Pre-Implant Radiographic Imaging: A Structured Qualitative Synthesis of Performance Across Imaging Modalities and AI Architectures
dek: Purpose: 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.
Materials and methods: 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 u…
gain_title: AI tools applied to pre-implant radiographs, mostly CBCT, performed well on segmentation and anatomical analysis and frequently shortened workflow time.
problem_title: Evidence for AI-driven implant positioning and outcome prediction is limited and heterogeneous, and most assessed studies carried a high risk of bias.
trace_subject: AI analysis of radiographic imaging for pre-implant dental planning
gain_reading: AI tools applied to pre-implant radiographs, mostly CBCT, performed well on segmentation and anatomical analysis and frequently shortened workflow time.
gain_evidence: Segmentation and anatomical-analysis studies generally reported high task-specific performance | AI frequently reduced workflow time.
problem_reading: Evidence for AI-driven implant positioning and outcome prediction is limited and heterogeneous, and most assessed studies carried a high risk of bias.
problem_evidence: Evidence for autonomous implant positioning and outcome prediction remains limited and heterogeneous. | four had high overall risk of bias and one unclear risk
quick_read: A systematic review covering studies from 2021 to 2025 examined how artificial intelligence uses radiographic images, chiefly CBCT, to support dental implant assessment and planning. Thirty-six studies were included and sorted by task, such as segmentation, bone assessment, and implant-position prediction.

The review matters because it separates technical algorithm accuracy from outcomes that affect clinical planning. Strong results on narrow image-analysis tasks did not carry over consistently to positional accuracy, and the reviewers call for external multicenter testing and prospective evaluation before routine use.
limitation: Heterogeneity prevented quantitative pooling, most assessable studies had high risk of bias, and evidence for autonomous positioning and outcome prediction is limited and needs external and prospective testing.
tag: Dual reading
key_points: Thirty-six peer-reviewed studies published between January 2021 and December 2025 were included. | CBCT was the most common imaging modality across the included studies. | Segmentation and anatomical analysis showed high task-specific performance, while positional accuracy and planning outcomes varied more. | Of five studies assessed with QUADAS-3, four had high risk of bias and one unclear risk. | The authors support clinician-supervised integration rather than autonomous AI planning.
rundown: The authors searched PubMed/MEDLINE, the Cochrane Library, ScienceDirect, JSTOR, and supplementary sources. Eligible studies used radiographic imaging as an integral AI input for pre-implant assessment, planning, decision support, virtual positioning, or outcome prediction.

Applications spanned anatomical detection and segmentation, bone assessment, decision support, implant-position prediction, automated virtual placement, workflow integration, and preoperative outcome prediction. Reporting completeness was judged with CLAIM 2024.

Risk of bias was judged with tools matched to study design: QUADAS-3 for diagnostic accuracy studies, PROBAST+AI for the prognostic model, and RoB 2 for the randomized study. The prognostic and randomized studies also had high risk of bias, and heterogeneity meant findings were synthesized qualitatively rather than pooled.
sources:
- peer_reviewed | The International Journal of Oral & Maxillofacial Implants | https://doi.org/10.11607/jomi.11913 | 2026-10-19
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