SynthesisBMC oral health2026
AI in orthodontic treatment planning: a systematic review comparing learning approaches.
Synthesis in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.The Saudi dental journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence has increasingly been applied to orthodontic diagnosis and treatment planning. Clinical decision-making in orthodontics is complex and often varies among practitioners, particularly for decisions such as tooth extraction, skeletal classification, and selection of treatment modality. This systematic review aimed to evaluate how artificial intelligence and knowledge-based systems have been developed and assessed for orthodontic treatment planning, to compare their performance with expert clinicians, and to examine how different learning approaches influence accuracy, interpretability, and potential clinical integration.
methodsA comprehensive search of PubMed, Embase, Scopus, Web of Science, Cochrane Library, and IEEE Xplore was conducted from database inception to 15 October 2025 without language or date restrictions. Eligible studies evaluated artificial intelligence-based or knowledge-based systems using real orthodontic patient data to support diagnosis or treatment planning. Two reviewers independently screened records, extracted data, and assessed risk of bias using the ROBINS-I tool. Due to heterogeneity in study designs, decision tasks, and outcome measures, findings were synthesized narratively rather than through meta-analysis.
resultsNineteen studies met inclusion criteria. Most investigated supervised machine-learning models for extraction decisions, skeletal classification, or multi-step treatment planning, while others evaluated rule-based, fuzzy, Bayesian, or hybrid systems. Reported accuracies frequently exceeded 80%, and in some studies surpassed 90% when compared with expert decisions. Knowledge-based systems, including rule-based, fuzzy, and Bayesian/probabilistic approaches, offered greater transparency in reasoning, whereas data-driven models often demonstrated higher discriminative performance. Overall risk of bias was predominantly moderate to serious, largely due to retrospective designs, single-center samples, limited external validation, and reliance on expert opinion as the reference standard.
conclusionsArtificial intelligence systems can approximate expert orthodontic decision-making for defined diagnostic and planning tasks and show promise as clinical decision-support tools. However, current evidence is limited by methodological weaknesses and lack of prospective validation. These systems should presently be considered adjunctive aids rather than autonomous planners. Robust multicenter studies with external validation are needed before routine clinical implementation can be recommended.
trial registrationPROSPERO 1025581. Registered prospectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.