## 4 Discussion
The model attempted to differentiate moles from healthy eyes; however, the limited number of healthy eye images and the small size of the moles contributed to the reduced accuracy of the model. Similar to Zabor, Valmaggia, Ma, and Tailor, no enhancement techniques were applied to the dataset. Finally, no study has validated the AI models on larger multicenter datasets, which is crucial for confirming their reliability and clinical utility. Despite these limitations, the potential of AI models to contribute to the early detection of melanoma, the reduction in physician workload, and the standardization of diagnostic accuracy is significant. However, it is essential to emphasize that AI should serve as a complement to, rather than a replacement for clinical judgment. Physicians should play an active role in AI training, validation, and real-world implementation to ensure confidence in its applicability in clinical settings. Nevertheless, it is imperative to address several challenges before these models can be fully integrated into clinical practice. A significant number of studies were based on single-center data, thereby limiting the generalizability of their results, and raising concerns about potential bias in AI predictions. The conduction of real-world validation trials is therefore essential in order to confirm AI performance beyond controlled research settings. Future studies should incorporate diverse, multicenter datasets to improve robustness and generalizability. Furthermore, most studies relied on retrospective data; although useful for model development, this does not guarantee real-world applicability. Prospective trials in diagnostic centers specializing in the management of choroidal lesions are necessary to evaluate AI performance in routine clinical practice. Another key future direction is the integration of imaging data with each patient's genetic profile to enhance diagnostic accuracy. Genetic databases of choroidal melanomas have already been used in studies aiming to personalize treatment and identify risk factors for malignant transformation through various algorithmic models. AI-driven multi-omics fusion networks could analyze patterns across imaging and genetic data, improving early detection, risk prediction, and personalized treatment strategies, paving the way for precision medicine in ocular oncology. Additionally, future research should focus on expanding data diversity, incorporating multimodal imaging (e.g., US, OCT, and UWF), and utilizing explainability tools to enhance transparency and improve interpretability. Additionally, advanced DL models, including Vision Transformers and generative adversarial networks (GANs), have shown promise in medical imaging applications, particularly for rare diseases where data availability is a challenge. Exploring these architectures could further improve melanoma detection and risk assessment, enhancing the robustness and clinical applicability of AI-driven models. Beyond algorithmic improvements, AI's integration into clinical workflows must address practical implementation challenges, such as cost, interoperability with existing systems, clinician acceptance, and training requirements. While AI-based tools can be valuable for ophthalmologists, their practical deployment remains unexplored. Understanding how these models can be widely adopted, from automated screening to decision-support systems, is essential for their successful implementation. AI-driven solutions should not only improve diagnostic accuracy but also be accessible, cost-effective, and seamlessly integrated into clinical practice. To ensure the successful integration of these AI-driven approaches into clinical practice, regulatory and ethical considerations must also be addressed. The recent EU AI Act regulations introduced new compliance requirements for AI systems in healthcare, emphasizing transparency, accountability, and patient safety [33]. Another key consideration is patient data privacy and security.