The early detection of incipient dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior incipient caries. In the field of medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched and has shown promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for dental caries detection on bitewing radiographs and investigated whether this model can improve clinicians’ performance. In total, 304 bitewing radiographs were used to train the deep learning model and 50 radiographs were used for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected dental caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased recall ratio (D1, 85.34%; D1', 92.15%; D2, 85.86%; D2', 93.72%; D3, 69.11%; D3', 79.06%). These increases were especially significant in the incipient and moderate caries subgroups. The deep learning model may help clinicians to diagnose dental caries more accurately.