AI analysis of radiographic imaging for pre-implant dental planning

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

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…

By the Truvace deskOctober 19, 20262 min read1 sourcePeer-reviewedTRV-2026-1939· cite & verify

Systematic Review of Artificial Intelligence in Pre-Implant Radiographic Imaging: A Structured Qualitative Synthesis of Performance Across Imaging Modalities and AI Architectures — "22-0008-010 Camp Lejeune" by NavyMedicine is marked with Public Domain Mark 1.0. To view the terms, visit https://creativecommons.org/publicdomain/mark/1.0/.

Trace impact readingNegative state

P 73

Both sides are scored from claims and sources, not community votes.

G 68

In brief

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.

Main 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.

The gain

AI tools applied to pre-implant radiographs, mostly CBCT, performed well on segmentation and anatomical analysis and frequently shortened workflow time.

The problem

Evidence for AI-driven implant positioning and outcome prediction is limited and heterogeneous, and most assessed studies carried a high risk of bias.

The 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.

What this doesn’t fix

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.