[Research and application of artificial intelligence in tooth extraction]

Tooth extraction is a common procedure in oral clinical practice. However, imaging interpretation, risk assessment, and perioperative management remain challenging for complex cases. In recent years, artificial intelligence(AI) has been increasingly applied to oral image recognition, anatomical structure segmentation, extraction difficulty assessment, complication risk prediction, surgical planning, and robot-assisted surgery, providing new approaches for improving clinical efficiency and supporting clinical decision-making. Nevertheless, several limitations remain in current studies. The identification of high-risk anatomical structures does not directly translate into actual surgical risk, model prediction endpoints are often disconnected from real-world clinical outcomes, insufficient coverage of rare imaging features limits the clinical applicability of existing models and problems related to uneven data quality, algorithmic limitations, and study design defects coexist. In addition, issues concerning clinical accessibility, responsibility delineation, and ethical regulation have also begun to emerge. We believe that AI should be positioned as a physician-led clinical assistive tool rather than an independent decision-maker. Future research should focus on the construction of high-quality datasets covering rare imaging features, multicenter prospective validation based on clinical outcomes, and the establishment of standardized human-machine collaboration frameworks, so that AI can truly contribute to enhanced safety, optimized clinical workflows, and better patient outcomes in complex tooth extraction.