We sincerely thank the author of the letter for their interest in our study and for their insightful and balanced evaluation of our findings. We fully concur with the key points raised in the letter. As correctly emphasized, the primary objective of our study was not to propose ChatGPT-4.0 as a diagnostic alternative for clinicians or radiology-specific artificial intelligence (AI) models. Rather, it was to objectively assess its current diagnostic performance and limitations when applied to highly specialized orthodontic tasks using panoramic radiographs.

The low accuracy rates observed in both the labiolingual localization of impacted maxillary canines and the detection of incisor root resorption were anticipated and align with our predefined null hypothesis. These findings reinforce our conclusion that although ChatGPT-4.0 possesses image-input capabilities, it lacks the domain-specific training necessary for reliable radiologic pattern recognition and is therefore not suitable for clinical diagnostic use at this stage.

Furthermore, we appreciate the recognition of our structured prompt optimization strategy, using iterative prompt calibration and multiple-choice formats. As demonstrated in our results, although these approaches contribute to improving response consistency and reducing hallucinations, they remain insufficient to reach clinically acceptable performance thresholds. This supports the notion that prompt engineering cannot compensate for fundamental training limitations when large language models are applied outside their intended scope.

Finally, we agree that our findings underscore the critical need for domain-specific AI models—particularly convolutional neural networks—and the importance of clear regulatory frameworks and defined clinical responsibility in the integration of AI into dental radiology.

We thank the author for this valuable contribution.