\begin{table}
\begin{tabular}{l l} \hline
**MOLES** & **TFSOM-UHHD** \\ \hline M: Mushroom shape & T: Thickness \textgreater{} 2 mm \\ O: Orange pigment & F: Fluid (subretinal) \\ L: Large size & S: Symptoms \\ E: Enlargement & O: Orange pigment \\ S: Subretinal fluid & M: Margin \(\leq\) 3 mm to disc \\  & UH: Ultrasonicographic hollowness \\  & H: Halo absence \\  & D: Drusen absence \\ \hline \end{tabular}
\end{table} 
Table 1: Clinical Tools—Scoring Systems. risk. DL algorithms, particularly convolutional neural networks (CNNs), can identify previously unobserved features that may indicate malignant transformation surpassing conventional diagnostic methods [13; 14]. CNNs, play a crucial role in medical image analysis by extracting hierarchical features from images. A typical CNN architecture consists of an input layer that processes retinal images, followed by multiple convolutional layers that apply filters to highlight relevant patterns. Pooling layers reduce data dimensionality while preserving important features, and fully connected layers generate final classification results. The structured way of learning and applying features allows the CNN to learn increasing levels of features and effectively detect and classify patterns in the images [15]. Various AI models have been applied in the studies selected for this review. DeepLabv3 is an advanced semantic image segmentation model that improves feature extraction by incorporating atrous (dilated) convolution. This technique allows the model to control the field of view of convolutional filters without increasing the number of parameters, making it efficient for detecting objects at multiple scales [16]. Residual networks (ResNet) utilize residual learning to train deep networks beyond 100 layers [17]. SAINT (Simple AI Nevus Transformation System) represents a hybrid DL approach for tabular data, integrating the strengths of transformers and contrastive learning, offering an alternative to traditional gradient boosting models such as XGBoost, CatBoost, and Light Gradient Boosting Model (LGBM). SAINT employs self-attention mechanisms and intersample attention to improve performance on structured datasets [18]. For segmentation tasks, U-Net, a fully convolutional network, has demonstrated high accuracy in biomedical image segmentation, even with limited training data [19]. nnU-Net, a further refinement, optimizes key parameters, including network architecture, training, and post-processing techniques, making it widely regarded as the gold standard for medical imaging segmentation [20]. DenseNet, a densely connected convolutional network, enhances performance through feature reuse across layers in a feedforward architecture [21]. RETFound is a self-supervised learning (SSL) foundation model designed for retinal image recognition. It is pre-trained on 1.6 million unlabeled fundus images, allowing it to learn generalizable features that can be adapted to multiple ophthalmic applications through transfer learning [22]. Additionally, LASSO (Least Absolute Shrinkage and Selection Operator) is a widely used statistical technique that performs variable selection and normalization to improve the predictive accuracy and interpretability of high-dimensional data, including genomics and ML applications [23]. Despite significant advancements, AI applications in CN and CM assessment still face challenges, including dataset limitations, model interpretability, and the need for external validation before clinical use. Addressing these barriers is essential for AI to transition from research settings to routine ophthalmological practice. This review evaluates the role of AI in accurate differentiation between CN and CM, focusing on diagnosis, classification, segmentation, and prediction of malignant transformation.