Deep learning, a subset of machine learning, has gained significant attention in medical imaging due to advancements in big data, computing power, and algorithms. It mimics the human brain's structure and function, enabling complex pattern recognition. Initially limited by issues like vanishing gradients and overfitting, deep learning has improved with better data, GPUs, and training techniques. It shows promise in tasks like lesion detection and classification, potentially enhancing diagnostic accuracy and efficiency in radiology. Deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are applied in various medical imaging tasks such as segmentation, registration, and automatic captioning. CNNs excel in image processing, while RNNs handle sequential data. These technologies have been used in radiology for tasks like detecting tumors, segmenting organs, and generating reports. Despite its potential, deep learning faces challenges such as data dependency, black-box nature, and ethical concerns. It requires large datasets and may lack interpretability, raising questions about its reliability in clinical settings. Legal and ethical issues also arise from using patient data for AI development. In radiology, deep learning can assist in computer-aided detection and diagnosis, improving efficiency and accuracy. However, it does not replace radiologists but augments their work by handling repetitive tasks. The integration of deep learning into healthcare aims to enhance precision medicine through big data analysis, enabling personalized treatment strategies. While deep learning is still in its infancy in radiology, its potential to transform diagnostic practices is significant. Collaboration between radiologists, computer scientists, and ethicists is crucial for its successful implementation. Future developments will depend on addressing technical, legal, and ethical challenges to ensure safe and effective use of AI in medical imaging.Deep learning, a subset of machine learning, has gained significant attention in medical imaging due to advancements in big data, computing power, and algorithms. It mimics the human brain's structure and function, enabling complex pattern recognition. Initially limited by issues like vanishing gradients and overfitting, deep learning has improved with better data, GPUs, and training techniques. It shows promise in tasks like lesion detection and classification, potentially enhancing diagnostic accuracy and efficiency in radiology. Deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are applied in various medical imaging tasks such as segmentation, registration, and automatic captioning. CNNs excel in image processing, while RNNs handle sequential data. These technologies have been used in radiology for tasks like detecting tumors, segmenting organs, and generating reports. Despite its potential, deep learning faces challenges such as data dependency, black-box nature, and ethical concerns. It requires large datasets and may lack interpretability, raising questions about its reliability in clinical settings. Legal and ethical issues also arise from using patient data for AI development. In radiology, deep learning can assist in computer-aided detection and diagnosis, improving efficiency and accuracy. However, it does not replace radiologists but augments their work by handling repetitive tasks. The integration of deep learning into healthcare aims to enhance precision medicine through big data analysis, enabling personalized treatment strategies. While deep learning is still in its infancy in radiology, its potential to transform diagnostic practices is significant. Collaboration between radiologists, computer scientists, and ethicists is crucial for its successful implementation. Future developments will depend on addressing technical, legal, and ethical challenges to ensure safe and effective use of AI in medical imaging.