Precision Medical Imaging and AI University of Cambridge

Overview

Precision Medical Imaging and AI from University of Cambridge has been developed to provide a multi-disciplinary taught course that focuses on the underlying principles of medical imaging technologies—including its physics, engineering, and mathematics—and their integration with state-of-the-art AI methodologies. 

The course will provide directly relevant professional training and research, educating suitably-qualified applicants through formal teaching and hands-on experience to prepare them for the future of precision imaging technologies across academic research, clinical practice, and industry.

The key educational objective of this course is to provide a comprehensive, interdisciplinary foundation in the physics, mathematics, and clinical applications of precision medical imaging techniques and AI. Students will develop through practical experience with exercises encompassing data acquisition, reconstruction, and critical evaluation across medical imaging modalities.

At the end of the course, the students will be expected to have:

• acquired a comprehensive knowledge of the physical principles underpinning medical imaging modalities, including MRI, CT, PET, SPECT, ultrasound, and emerging technologies, with a focus on instrumentation, data acquisition fundamentals, quantitative biomarkers, and clinical applications;

• acquired an in-depth understanding of image formation, reconstruction and processing techniques, including knowledge of imaging biomarkers, their extraction, validation, and visualisation;

• acquired a thorough knowledge of statistics relevant to medical imaging and AI, including diagnostic accuracy metrics, reproducibility assessments, multiple comparisons, regression, and validation frameworks;

• acquired expertise in machine learning methodologies tailored to precision medical imaging, including artificial neural networks, and image-based tasks like feature extraction, segmentation, denoising, classification, and prediction;

• acquired an appreciation of the interdisciplinary integration of imaging physics and computational analysis with clinical contexts, including patient anatomy, physiology, pathology, and evidence-based practices;

• acquired a familiarity with the regulatory and ethical frameworks governing medical imaging research and AI deployment;

• carried out practical work related to the subjects above, and produced written and computational reports of that work.