## 4 Discussion  
This literature review explores the role of AI in accurate differentiation of CN and CM, focusing on diagnosis, classification, segmentation, and the prediction of malignant transformation. Eight studies were selected, evaluating AI models on accuracy, sensitivity, specificity, Dice score, F1 score, and AUC. The variation of AUC values between studies ranged from 0.8800 to 0.9781, with the superior performance being attributed to the DenseNet 121 model (0.9781) in intermediate fusion for classification of UM and nevi. The studies by [PERSON] [30] and [PERSON] [32] evaluated AUCROC, achieving high values of 0.931 and 0.920, respectively. The heterogeneity of the studies is evident in sample size, patient numbers, imaging methods used to visualize choroidal lesions, and architectural models employed. As presented in Table 2, the models demonstrated an accuracy range of 83 to 92.65%. The study by [PERSON] [29] yielded the highest accuracy (92.65%) identifying ResNet 50 as the top-performing model among those evaluated. Nevertheless, the models by [PERSON] [28] and [PERSON] [30] attained remarkably high accuracy of 92.24% and 91%, respectively. The U-net and DeepLabv3 algorithms demonstrated high sensitivity for UM segmentation in fundus images, achieving a 100% success rate for lesion detection. Meanwhile, nnU-Net, used for OCT-based segmentation, outperformed MD-GRU and V-Net, achieving the highest recall (0.77) and Dice coefficient (0.78). Another performance metric that has been utilized in certain research studies is the F1 score [28,30,32] with the [PERSON] [28] study attaining the maximum value of 0.8788. The F1 score is a crucial evaluation metric in ML, particularly for imbalanced datasets where accuracy alone may provide misleading results. By combining precision and recall through their harmonic mean, the F1 score ensures a balanced assessment of a model's predictive performance. Since precision and recall exist in a trade-off, the F1 score provides a single, comprehensive metric that encourages optimization of both. Notably, the study by [PERSON] et al. [31] was the only one to compare the performance of the AI model with 12 ophthalmologists, who classified small choroidal melanomas and choroidal nevi based on the MOLES and TFSOM systems. The AI model showed superior sensitivity (\(p=0.006\)) in melanoma assessment compared to human experts, particularly outperforming resident and consultant ophthalmologists, but it did not significantly surpass ocular oncologists in specificity (\(p>0.99\)). As part of ongoing efforts to enhance AI capabilities, [PERSON]'s study was the only study to employ XAI techniques, using SHAP values to analyze the most significant features in predicting mole progression to melanoma. SHAP values are a technique used in ML to explain model predictions by quantifying the contribution of each feature to the final outcome. By calculating the effect of each feature across all possible feature combinations, SHAP values provide a detailed and consistent measure of the importance of features for any given prediction. This approach helps to understand complex models by clarifying which factors drive predictions, thereby improving interpretability, transparency, and trust in AI systems; an essential goal of XAI, particularly in critical healthcare applications. Despite the promising results demonstrated by AI models, several limitations and challenges must be addressed before full clinical integration. Most studies relied on limited datasets, potentially compromising model robustness. A notable exception was [PERSON]'s study, which used a large dataset of 27181 images from 4255 patients. However, even with a larger dataset, the risk of overfitting remains when compared to smaller studies. Additionally, external validation datasets were imbalanced, a factor that may impede the generalizability of the model. Most studies were conducted on a dataset from a single institution, introducing potential bias due to patient demographics, imaging protocols, and equipment variations.