Background The advent of artificial intelligence (AI) in dentistry holds promise for enhancing diagnostic accuracy. This study aims to compare the diagnostic performance of AI and junior dentists in detecting dental caries and periapical infections using panoramic images. Materials and Methods A total of 200 panoramic images were selected from the dental records of a tertiary care hospital. An AI system trained on a large dataset of annotated dental images was used to analyze the selected images. Simultaneously, a group of 10 junior dentists, with 1-3 years of clinical experience, independently evaluated the same set of images. The diagnostic performance was assessed by measuring sensitivity, specificity, and accuracy for both AI and junior dentists. The gold standard for diagnosis was established by consensus from three experienced dental radiologists. Results The AI system demonstrated a sensitivity of 92%, specificity of 89%, and accuracy of 90% in detecting dental caries. In contrast, the junior dentists showed an average sensitivity of 80%, specificity of 85%, and accuracy of 82%. For periapical infections, the AI system achieved a sensitivity of 95%, specificity of 90%, and accuracy of 92%, while the junior dentists had a sensitivity of 85%, specificity of 87%, and accuracy of 86%. Statistical analysis revealed that the AI system significantly outperformed the junior dentists in both diagnostic tasks (p < 0.05). Conclusion The findings suggest that AI has the potential to surpass the diagnostic capabilities of junior dentists in detecting dental caries and periapical infections on panoramic images. Integrating AI into dental practice could enhance diagnostic accuracy and support clinical decision-making.