Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Systematic Review of Deep Learning Techniques for Medical Diagnosis

Deep learning has advanced the development of technology in medical imaging, particularly in regards to the analysis of MRI scans. This area of Artificial Intelligence (AI) has opportunities to enhance the precision and speed of diagnosis by automating the recognition and segmentation processes in image analysis. Convolutional Neural Networks (CNN) is one example of a vast array of advanced deep learning models developed recently, which, over the past few years, have been able to accurately classify diseases. Nevertheless, the use of deep learning approaches on MRI imaging is known to face numerous challenges. Perhaps the most crucial challenge is the absence of reliable medical datasets. MRI imaging data is often inaccessible due to privacy issues linked with patient information, along with the diversity of imaging modalities employed by various medical institutions, which could limit the generalizability of the models. Furthermore, most algorithms based on deep learning require intensive computations to be carried out, which are not readily available in every healthcare unit, particularly in developing nations. Finally, another challenge is the model's lack of transparency, rendering the information provided to medical personnel incomprehensible and, therefore, clinically irrelevant. The objective of this work is to review the literature on the application of deep learning techniques to MRI imaging and analyze it with its conventional counterparts while outlining the challenges posed by their use. This will help researchers, doctors, and tech developers make better use of AI in diagnosing pathologies in MRI scans.