## 4 Discussion
Furthermore, the datasets consisted of predominantly white patients, which limits the ability to generalize across diverse populations. Anotherlimitation is the lack of histopathological or genetic confirmation, raising concerns about diagnosis accuracy. While all studies employed specific imaging device models for model training, cross-device performance was not evaluated, potentially limiting adaptability to different imaging systems. The majority of studies lacked longitudinal data, with the exception of [PERSON]'s study, as their primary focus was on initial diagnosis rather than long-term progression of lesions. Despite internal and external validation, models may still be affected by biases in training data. Furthermore, poor quality images (e.g., blurry, overexposed, or dark) were excluded, which may have biased the models toward high quality images. Consequently, their performance on lower quality real-world images remains uncertain, posing a significant risk. Another key limitation is the human annotation of choroidal lesions in each study, a process that introduces potential variability and impacts model training. Future studies should include multiple independent raters to improve annotation reliability. Additionally, model calibration remains a significant challenge. The Zabor model [25] exhibited a tendency to overestimate the risk of melanoma in the validation dataset, suggesting potential issues with model calibration. Furthermore, the examination of a limited set of clinical characteristics in this study may have resulted in the exclusion of other potential prognostic factors. Measurements such as tumor size and the presence of pigment were based on clinical examination, which may be subject to interobserver variability. In the Valmaggia study [26], three neural networks were tested, yet their hyperparameters remained unoptimized. This suggests potential areas for enhancement, indicating a possibility for further refinement. This limitation was identified in all the studies reviewed. Furthermore, a paucity of research was identified in the field of 2D versus 3D segmentation, a gap explored in studies such as Valmaggia, Ma, and Sabazade. The Ma study [27] also faced issues with image quality, including blurry images, lesions located at image boundaries, and small lesions overlapping the fovea. These factors adversely affected the model's accuracy and misclassification rates. The study was based on 2D fundus images, which means that lesion thickness, which is an important diagnostic factor, was not considered. Additionally, the Ma model was designed to detect lesions of a minimum size of 24 pixels, likely excluding very small lesions that may be of clinical significance. Similarly, in the Dadzie model [28], no image preprocessing techniques, such as contrast enhancement or cropping, were applied, which could have improved the model's performance. In the [PERSON] study [29], some borderline cases, especially those with moderate thickness and internal reflectivity, were misclassified by the DL model. A significant limitation of the [PERSON] study [30] was the inclusion of only the largest nevus per eye in patients with multiple nevi, resulting in the loss of crucial data. In the study, there was a strong concern about the risk of false negatives and false positives, as the failure to diagnose a melanoma could have serious consequences, while the erroneous diagnosis of a melanoma could lead to unnecessary interventions. The [PERSON] study [31] highlighted another important consideration: the model's accuracy depends on the clinical expertise of the ophthalmologists who initially diagnosed the lesions. Moreover, lesions classified as nevi may still have undetected malignant potential. The study used segmentation and classification techniques rather than an end-to-end DL model that might have enhanced performance. Lastly, the [PERSON] model [32] misclassified 11% of UM cases as moles, posing significant clinical implications. It also over diagnosed moles as UM, increasing the risk of unnecessary referrals.