• Deep learning models, especially transformer-based architectures, achieve human-level performance in medical image classification and segmentation tasks.
• Data scarcity and class imbalance remain critical challenges, mitigated by techniques such as data augmentation, synthetic data generation, and self-supervised learning.
• Federated learning enables collaborative model training across institutions without compromising patient privacy, enhancing generalizability.
• Interpretability methods, including attention maps and saliency, are essential for clinical trust and regulatory approval.