This systematic review evaluates the diagnostic performance of artificial intelligence (AI) models for detecting caries lesions (CLs) in dental radiographic imaging. Twenty studies were included, using periapical radiographs (n=5), bitewings (n=9), and orthopantomography (n=6). The studies employed various AI algorithms, including artificial neural networks (ANNs), convolutional neural networks (CNNs), and deep convolutional neural networks (DCNNs). Diagnostic performance metrics such as sensitivity (0.44–0.86), specificity (0.85–0.98), accuracy (0.73–0.98), and area under the curve (AUC) (0.84–0.98) were reported. Most studies showed low risk of bias according to QUADAS-2 guidelines. AI-based models demonstrated good diagnostic performance, potentially aiding in CL detection. However, limitations include dataset size and heterogeneity. Future studies should use comparable, large, and clinically meaningful datasets. AI has the potential to improve diagnostic accuracy, reduce workload, and enhance clinical decision-making in dentistry.This systematic review evaluates the diagnostic performance of artificial intelligence (AI) models for detecting caries lesions (CLs) in dental radiographic imaging. Twenty studies were included, using periapical radiographs (n=5), bitewings (n=9), and orthopantomography (n=6). The studies employed various AI algorithms, including artificial neural networks (ANNs), convolutional neural networks (CNNs), and deep convolutional neural networks (DCNNs). Diagnostic performance metrics such as sensitivity (0.44–0.86), specificity (0.85–0.98), accuracy (0.73–0.98), and area under the curve (AUC) (0.84–0.98) were reported. Most studies showed low risk of bias according to QUADAS-2 guidelines. AI-based models demonstrated good diagnostic performance, potentially aiding in CL detection. However, limitations include dataset size and heterogeneity. Future studies should use comparable, large, and clinically meaningful datasets. AI has the potential to improve diagnostic accuracy, reduce workload, and enhance clinical decision-making in dentistry.