\section{Discussion and Conclusions}
We introduce \emph{SemiSynCXR}, a framework for automatically generating localization datasets for chest radiological findings. Our framework's core strength lies in its ability to provide the generated images with intrinsically matching, precise bounding boxes at scale. Extensive evaluations confirm that the quality and realism of the edited images are comparable to fully synthetic data, while simultaneously demonstrating their utility as a training data augmentation source. Our findings suggest that \emph{SemiSynCXR} provides a practical and effective solution to addressing data scarcity in medical imaging.

Nonetheless, certain limitations remain. Editing quality is constrained by both the capabilities of the underlying diffusion models and the effectiveness of our mask generation strategy. Although model performance could be enhanced through fine-tuning, improving mask conditioning is more challenging. Specifically, the bounding-box-constrained editing approach, while beneficial for maintaining structural integrity and providing precise ground truth annotations, may fail to fully capture the changes when findings extend beyond their localized bounding boxes, potentially struggling to represent diffuse conditions accurately. Promising future directions for addressing these limitations include the implementation of iterative mask relaxation and the use of anatomical region bounding boxes. Additionally, our current framework does not explicitly account for finding size and severity, which are instead influenced by the sampled editing mask. Integrating explicit control over these attributes would enable more nuanced generation guidance.

A larger-scale medical expert study is also essential to further validate the clinical realism and utility of the generated data. Similarly, large-scale multi-center evaluations would be beneficial for identifying potential dataset biases. Among the framework's components (i.e., healthy input CXRs, editing-conditioning elements, and the underlying model), the healthy input X-rays possess the highest potential as a source of dataset-specific biases. Consequently, the inclusion of more diverse CXR datasets within these components is essential to strengthen the framework's robustness across varied clinical settings.

Expanding SemiSynCXR to support additional radiological findings, such as calcifications, fractures, and nodules, is a natural extension that primarily requires the probability distributions of the bounding boxes for such findings. Beyond chest X-rays, this approach is applicable to other clinical conditions characterized by rather focal, non-diffuse manifestations. Examples include tumors and lesions in oncology, aneurysms and hemorrhages in cardiovascular imaging, and drusen in ophthalmology. The framework is also potentially adaptable to other 2D and 3D imaging modalities, as well as to time series of images. Such extensions require an existing pretrained text-conditioned diffusion model (or data to train one) and the probability distributions of the bounding boxes.

A strategic advantage of our framework is its ability to leverage pretrained models, thereby benefiting from ongoing advancements in the field without the need for training from scratch. Even if the underlying model must be trained, our approach remains viable since training relies only on image-text pairs (derivable from vision-language models) rather than manual bounding box annotations. All of these factors demonstrate our framework’s broad applicability and potential for continued development. Finally, we see great opportunities in modeling rare clinical cases, which could significantly enhance the robustness of automated object detectors and provide substantial clinical utility.