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Medical Imaging, which allows for the non-invasive assessment of biological tissues, is a rapidly growing health care service that has evolved from a diagnostic tool to a platform for personalized precision medicine. Computerized Tomography (CT), which is a commonly obtained imaging study worldwide, utilizes X-ray radiation to differentiate tissues based on density differences. More complex imaging studies are obtained based on the clinical question to supplement the CT. Image overutilization, which is the acquisition of medical images that have minimal impact on patient care and increasing global inequities due to the cost/availability of medical imaging services are significant problems that need to be addressed. One possible solution would be to maximize the amount of clinically relevant information extracted from routine CT images using machine learning approaches. Recent developments in machine and deep learning have provided a powerful set of tools for the automated and complex analysis of medical images. Leveraging these techniques, it is possible to develop algorithms capable of learning from human annotations or paired images. These methods can also be applied to large datasets to minimise human input and to enable the rapid scale up of analyses. In this thesis, I hypothesize that Non-Contrast CT images contain higher-order information to differentiate tissue anatomy or pathology without the need of intravenous contrast agents or radioactive tracers. I further hypothesized that such higher-order information enables stratification of disease progression without the need of additional imaging studies. I focus on two pathologies, abdominal aortic aneurysms (AAA) and Head and Neck Squamous Cell Carcinoma (HNSCC) as demonstration of feasibility. The methods described in this thesis can be applied to other pathologies and will be poised to disrupt clinical pathways in the future.