Medical Imaging and AI (MSc) MSc

Medical imaging is the technology that allows us to see inside the human body without surgery.<br/><br/>This involves techniques such as MRI, CT, ultrasound, and advanced digital imaging. Using these clinicians can detect disease earlier and plan treatments with precision. They can also monitor how well therapies are working.<br/><br/>Medical imaging plays a vital role in modern healthcare. It supports accurate diagnosis, improves patient outcomes, and guides life-saving decisions.<br/><br/>Artificial intelligence (AI) is reshaping every part of healthcare.<br/><br/>By analysing large sets of medical data, AI can recognise patterns that are too complex or subtle for humans to detect.<br/><br/>In imaging, AI systems can enhance image quality and detect abnormalities automatically. It can also support clinical decisions, and streamline workflows.<br/><br/>AI is not replacing clinicians. It is becoming a powerful tool that helps them work faster, more accurately, and with greater confidence.<br/><br/>When combined, medical imaging and AI create one of the most exciting frontiers in modern medicine.<br/><br/>AI-driven image analysis can speed up diagnosis, reduce human error, and expand healthcare access around the world.<br/><br/>This intersection is driving breakthroughs in healthcare. These range from early cancer detection to personalised treatment planning. These all have a positive impact on patient care.<br/><br/>In this course, you will learn how imaging science, advanced technology, and AI algorithms come together. You will see how they come together to solve real clinical problems.<br/><br/>Instead of studying these areas in isolation, you will think across imaging physics, computational methods, and clinical decision-making. This mirrors how innovation happens in modern medical imaging.<br/><br/>You will build a number of practical skills. This will be done through hands-on labs, project-based learning, and exposure to real imaging workflows. You will learn to:<br/><br/><br/>- understand and optimise imaging techniques, including MRI, CT, ultrasound, and multimodal systems<br/><br/><br/>- apply AI and machine learning to images, signals, and clinical datasets<br/><br/><br/>- understand quantitative imaging, segmentation, reconstruction, and computer-assisted diagnosis<br/><br/><br/>- evaluate imaging technologies in clinical environments. You will also learn to understand their diagnostic impact<br/><br/><br/>- navigate regulatory, safety, and ethical requirements in imaging and AI development<br/><br/><br/>- working effectively with radiologists, physicists, engineers, and AI specialists. This will allow you to design clinically relevant solutions<br/><br/><br/>Graduates from this course will be prepared for a number of roles such as in<br/><br/><br/>- medical imaging companies<br/><br/><br/>• AI healthcare organisations<br/><br/><br/><br/>- the NHS<br/><br/><br/>- research institutes<br/><br/><br/>- start-ups<br/><br/><br/>- international health technology sectors<br/><br/><br/>This MSc provides the interdisciplinary foundation to thrive in a rapidly advancing field.<br/><br/>At the beginning of the course, you will develop a strong foundation in imaging principles.<br/><br/>This will involve learning about modalities such as MRI, CT, ultrasound, and nuclear medicine.<br/><br/>You will learn how they acquire and represent information about the human body.<br/><br/>You will explore safety, instrumentation, and image formation. You will also cover the clinical roles of each technology.<br/><br/>As you advance, you will study image processing, quantitative imaging, and computational methods.<br/><br/>You will learn how these are used to enhance, analyse, and interpret medical images.<br/><br/>You will be introduced to tools and software used across clinical and research environments. This will be done through practical sessions.<br/><br/>AI is woven throughout the course.<br/><br/>You will learn how machine learning and deep learning models are applied to medical images. This will cover topics such as early disease detection to automated segmentation. It will also cover classification, and workflow optimisation.