Medical Imaging Application

Medical imaging applications are rapidly advancing through the integration of artificial intelligence, aiming to improve diagnostic accuracy, treatment planning, and patient care. Current research emphasizes developing robust, trustworthy AI models, focusing on addressing biases in data and algorithms, enhancing privacy through techniques like federated learning, and improving model interpretability using methods such as Grad-CAM. Large language models and novel architectures like spherical CNNs are being explored to improve performance and efficiency across various imaging modalities and tasks, including image segmentation, classification, and registration. These advancements hold significant potential for improving healthcare by enabling faster, more accurate, and equitable diagnoses.