## 5 Conclusions  
The results of this study show that AI has shown high potential in multiple areas of orbital pathologies. In particular, deep learning models based on CT or MRI images have enabled the accurate classification of orbital fractures and tumors, benign and malignant, with AUC and accuracy values 90% higher in multiple studies. In parallel, transcriptomic analysis using machine learning models has enabled the identification of distinctive biomarkers for inflammatory diseases such as NSOI, suggesting clinical, diagnostic, and therapeutic implications with a view to increasingly personalized medicine.  
While CT and MRI are the most thoroughly researched modalities, novel AI applications in other imaging fields, including ultrasonography, angiography, and carotid arteriography, have shown encouraging potential. Recent research has employed convolutional neural networks (CNNs) to examine orbital ultrasonographic data for the swift differentiation of cystic and solid masses. Likewise, AI-augmented angiography has been investigated for identifying arterial abnormalities and perfusion deficiencies in orbital tissues.  
The integration of AI has shown advantages in terms of speed, standardization, and reduction in human error, particularly in diagnostic-intensive settings or in settings where specialized expertise is limited. However, the discussion also highlights the main critical issues that have emerged, including the scarcity of large, multicenter datasets, the absence of prospective studies, and the limited use of this technology in real-world clinical practice. Moreover, few studies truly integrate multimodal data (i.e., clinical, radiological, and histological), an important aspect for the full adoption of these tools in complex decision making.  
The utilization of generative AI models, including GANs and diffusion-based networks, is poised to substantially improve orbital diagnostics. These models may be employed to produce synthetic imaging datasets for uncommon orbital disorders, develop resilient segmentation models, and forecast anatomical reconstructions with high accuracy for surgical planning. Their incorporation into clinical practice may expedite individualized therapy planning and enhance accessibility in data-deficient settings.  
AI thus emerges as a strategic resource in the management of these diseases, with growing applications in diagnostic, predictive, and therapeutic fields. The results, to date, indicate significant potential for improving diagnostic accuracy and therapeutic personalization. However, to ensure true clinical translation of such technology, the current limitations must be overcome by promoting validated, multicenter, prospective studies, building heterogeneous datasets, and developing AI interfaces that can be integrated into clinical workflows.  
Conceptualization, L.M., A.T., M.R., G.C., E.A., T.I., C.G. and M.Z.; methodology, L.M., A.T., E.A., T.I., C.G. and M.Z.; validation, L.M., A.T., M.R., G.C., E.A., T.I., C.G. and M.Z.; formal analysis, L.M. and A.T.; investigation, L.M. and A.T.; resources, L.M., A.T., M.Z., T.I., E.A. and M.R.; data curation, L.M., A.T., M.R., G.C., E.A., T.I., C.G. and M.Z.; writing--original draft preparation, L.M. and A.T.; writing--review and editing, L.M. and A.T.; visualization, L.M., A.T., M.R., G.C., E.A., T.I., C.G. and M.Z.; supervision, A.T., C.G., M.Z. and M.R.; project administration, L.M., A.T., T.I., C.G. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** The data are available upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.