Oral surgery, oral medicine, oral pathology and oral radiology · 2025
Augmented intelligence in oral and maxillofacial radiology: a systematic review.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Math Swarna Yerebairapura, Ameli Nazila, Stefani Cristine Miron, Kung Janice Y, Punithakumar Kumaradevan, Amin Maryam, Pacheco-Pereira Camila
Аннотация
BACKGROUND: Artificial intelligence (AI) is transforming diagnostic imaging in dentistry. This systematic review evaluates existing literature on augmented intelligence in dentomaxillofacial radiology, focusing on its influence on human collaboration in interpreting dental imaging.
STUDY DESIGN: A literature search across seven databases and gray literature was conducted. Studies evaluating clinician performance with AI-assistance were included, while reviews, surveys, and case reports were excluded. The QUADAS-2 tool assessed the risk of bias.
RESULTS: Sixteen studies assessed the influence of AI on radiographic interpretation. AI-assisted caries detection consistently improved accuracy, sensitivity, and specificity. Detection of apical pathoses and jaw lesion segmentation improved accuracy, reducing diagnostic time. Cephalometric landmark identification showed increased accuracy, particularly for students. Soft tissue calcification detection improved accuracy, but sensitivity decreased. Overall, augmented intelligence enhanced interobserver agreement and reduced diagnostic variability, with general dentists and students showing the greatest gains.
CONCLUSIONS: Augmented intelligence enhances dental radiographic interpretation by improving tasks, particularly for less experienced clinicians, and positively influences clinical decision-making. However, AI performance remains inconsistent in challenging cases involving complex pathoses or varied imaging conditions. While it complements rather than replaces clinicians, further validation of AI's generalizability and reliability using larger, diverse datasets is necessary.
STUDY DESIGN: A literature search across seven databases and gray literature was conducted. Studies evaluating clinician performance with AI-assistance were included, while reviews, surveys, and case reports were excluded. The QUADAS-2 tool assessed the risk of bias.
RESULTS: Sixteen studies assessed the influence of AI on radiographic interpretation. AI-assisted caries detection consistently improved accuracy, sensitivity, and specificity. Detection of apical pathoses and jaw lesion segmentation improved accuracy, reducing diagnostic time. Cephalometric landmark identification showed increased accuracy, particularly for students. Soft tissue calcification detection improved accuracy, but sensitivity decreased. Overall, augmented intelligence enhanced interobserver agreement and reduced diagnostic variability, with general dentists and students showing the greatest gains.
CONCLUSIONS: Augmented intelligence enhances dental radiographic interpretation by improving tasks, particularly for less experienced clinicians, and positively influences clinical decision-making. However, AI performance remains inconsistent in challenging cases involving complex pathoses or varied imaging conditions. While it complements rather than replaces clinicians, further validation of AI's generalizability and reliability using larger, diverse datasets is necessary.