Artificial intelligence-based image analysis is transforming medical diagnosis; however, its clinical usefulness is limited by inherent noise and the quality of available medical datasets. Recent advances in generative AI promise to improve image quality through denoising, enhanced resolution, and more. This study applies the Segment Anything Model 2 (SAM2) and the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to dental radiographic datasets. A dataset of 6,413 radiographs across 35 dental implant systems was used to explore the pre-processing paradox, the relationship between human visual clarity and machine-learning accuracy. First, YOLOv11 detected and cropped implant areas, establishing the baseline for two classification models: ConvNeXt and ResNet50. These were compared with three experimental variants: SAM2 segmentation, ESRGAN super-resolution, and a fully integrated YOLO+SAM2 + ESRGAN pipeline. Performance was assessed using accuracy, F1-score, PSNR, and SSIM. In the baseline YOLO+CNN approach, the accuracy of ConvNeXt (83%) outperformed that of ResNet50 (79%). Adding SAM2 segmentation lowered performance (ConvNeXt: 81%, ResNet50: 76%), suggesting that removing the bone-implant interface eliminates important contextual signals. Both ConvNeXt and ResNet maintained their average accuracies (83 and 80%) when ESRGAN was applied to raw and cropped images. The largest difference appeared in the fully integrated pipeline, where ConvNeXt achieved higher accuracy (82%) compared to a significant decline in ResNet performance (64%). The study confirms the pre-processing paradox. While SAM2 and ESRGAN enhance visual clarity, the artifacts they introduce can reduce ResNet’s accuracy. The YOLOv11-CNN approach is optimal, and ConvNeXt is recommended for its robustness to image preprocessing. However, the proposed preprocessing pipeline did not significantly improve accuracy.