Deep learning has significantly advanced medical imaging, particularly in MRI. This overview discusses recent developments and challenges in applying deep learning to medical image processing and analysis, with a focus on MRI. Deep learning, based on artificial neural networks, has become the state-of-the-art in various fields, including computer vision, natural language processing, and speech recognition. In medical imaging, deep learning is used for tasks such as image segmentation, disease prediction, and diagnostic support. It has shown great potential in improving the accuracy and efficiency of medical diagnostics and healthcare. The use of deep learning in healthcare is still in its early stages, but there are many initiatives and companies exploring its applications. Deep learning techniques, such as convolutional neural networks (CNNs), are particularly effective in medical imaging due to their ability to automatically learn features from raw data. CNNs are used for tasks such as image restoration, denoising, artifact detection, and super-resolution. They are also applied in image registration, enabling quantitative analysis across different imaging modalities. Deep learning has been used in various applications, including radiology, radiotherapy, PET-MRI, and radiomics. The field is rapidly evolving, with many new techniques and frameworks being developed. The use of deep learning in medical imaging is expected to continue growing, offering new opportunities for improving patient care and outcomes.Deep learning has significantly advanced medical imaging, particularly in MRI. This overview discusses recent developments and challenges in applying deep learning to medical image processing and analysis, with a focus on MRI. Deep learning, based on artificial neural networks, has become the state-of-the-art in various fields, including computer vision, natural language processing, and speech recognition. In medical imaging, deep learning is used for tasks such as image segmentation, disease prediction, and diagnostic support. It has shown great potential in improving the accuracy and efficiency of medical diagnostics and healthcare. The use of deep learning in healthcare is still in its early stages, but there are many initiatives and companies exploring its applications. Deep learning techniques, such as convolutional neural networks (CNNs), are particularly effective in medical imaging due to their ability to automatically learn features from raw data. CNNs are used for tasks such as image restoration, denoising, artifact detection, and super-resolution. They are also applied in image registration, enabling quantitative analysis across different imaging modalities. Deep learning has been used in various applications, including radiology, radiotherapy, PET-MRI, and radiomics. The field is rapidly evolving, with many new techniques and frameworks being developed. The use of deep learning in medical imaging is expected to continue growing, offering new opportunities for improving patient care and outcomes.