Artificial Intelligence in Oral and Maxillofacial Surgery: Integrating Clinical Innovation and Workflow Optimization

TL;DR

This paper reviews how AI is being used in oral and maxillofacial surgery to improve diagnostics, planning, and clinic operations, while highlighting the need for validation and responsible adoption.

Contribution

The paper uniquely combines clinical and operational AI applications in OMFS, quantifying evidence and validation status for practical adoption guidance.

Findings

AI achieves up to 96% predictive accuracy in radiographic analysis and sub-millimeter soft-tissue simulation error in virtual planning.

Early AI deployments in OMFS clinics increased appointment bookings and patient satisfaction while reducing administrative burden.

Remaining challenges include data quality, explainability, and limited multicenter validation, which affect generalizability.

Abstract

Objective: The objective of this study is to synthesize and critically appraise how artificial intelligence (AI) is being integrated into oral and maxillofacial surgery (OMFS). This review’s novel contribution is to jointly map clinical applications (diagnostics, virtual surgical planning, intraoperative guidance) and operational uses (triage, scheduling, documentation, patient communication), quantifying evidence and validation status to provide practice-oriented guidance for adoption. Study Design: A narrative review of the recent literature and expert analysis, supplemented by illustrative multicenter implementation data from OMFS practice, was carried out. Results: AI demonstrates high performance in radiographic analysis and virtual planning (up to 96% predictive accuracy and sub-millimeter soft-tissue simulation error), with clinical reports of shorter planning times and more efficient patient communication. Early deployments in OMFS clinics have increased appointment bookings, while maintaining high patient satisfaction, and reduced the administrative burden. Remaining challenges include data quality, explainability, and limited multicenter and pediatric validation, which constrain generalizability and require clinician oversight. Conclusions: AI offers substantive benefits across the OMFS care continuum—improving diagnostic accuracy, surgical planning, and patient engagement while streamlining workflows. Responsible adoption depends on transparent validation, data governance, and targeted training, with attention to cost-effectiveness. Immediate priorities include standardized reporting of quantitative outcomes (e.g., sensitivity, specificity, time saved) and prospective multicenter studies, ensuring that AI augments—rather than replaces—human-centered care.

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