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In medicine, the high volume of available data and the expanded number of treatment options have rendered it increasingly complex to determine the appropriate therapy for a specific patient. Precision medicine is a promising and emerging approach to tailor disease prevention and treatment by taking into account individual patient characteristics. Medical imaging constitutes a rich and valuable source of patient information, but due to its unstructured nature, it requires prior processing to extract useful parameters. Currently, this analysis of imaging data often requires manual image interpretation which proves too cumbersome for clinical routine. Computer-aided diagnosis (CAD) systems can support physicians by performing fast, objective and reproducible image analyses and by extracting parameters that allow for more personalised disease assessment and response prediction, allowing to support therapeutic decisions. In this work, we have investigated tools for CAD and clinical decision support (CDS) systems for two pathologies, malignant melanoma and coronavirus disease 2019 (COVID-19). Malignant melanoma is the most lethal form of skin cancer. However, survival chances have increased since recent advances in immune checkpoint inhibitors and targeted therapies. Treatment planning and monitoring are generally performed using combined positron emission tomography/computed tomo- graphy (PET/CT) with fluorine-18 fluorodeoxyglucose ([18F]FDG) and regu- lar testing of blood values. Though guidelines like immune Positron Emission Tomography Response Criteria In Solid Tumors (iPERCIST) recommend quantitative analysis and comparison of target lesions, their application is currently not feasible in routine clinical practice. We developed a fully automated system for lesion detection and segmentation on whole-body [18F]FDG PET/CT which provides a fast, objective and reproducible way to extract information on the tumour load from the imaging data. Immunotherapy has shown to improve the prognosis of patients suffering from advanced melanoma with respect to the previous generation of treatments. Still, a considerable part of this population demonstrates progressive disease. If patients with a poor prognosis can be identified before the start of therapy, a more aggressive treatment pathway could be considered to improve the survival chances. Previous clinical research had identified several image- based and non-imaging predictors, which were used as a starting point. The contributions of this work are threefold. First, we demonstrated the feasibility of using automatically derived imaging features in survival analysis through a comparative study with the manual method. Next, we analysed the predictive value of organ-specific tumour loads that can be used to include additional features in future clinical research. As a third contribution, multivariate Cox regression models were developed to predict one- and two- year overall and progression-free survival for metastatic melanoma patients when treated with pembrolizumab, an immune checkpoint inhibitor. COVID-19 has presented great challenges for the medical sector worldwide. During the pandemic, intensive care units were overwhelmed and proper resource allocation became problematic. COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2). Patients can be asymptomatic or experience mild symptoms, while others develop very severe illness. During the periods of high prevalence, there was an urgent need for computer-aided systems to assist in diagnosis, treatment recommendation and resource allocation. In a large research collaboration, artificial intelligence (AI) tools were developed to alleviate the situation. We contributed to the development of automated methods for the segmenta- tion of lung lesions suggestive of COVID-19 on CT by investigating deep learning segmentation approaches. The resulting methods allow to obtain automated lung severity scores and can aid radiologists in quantifying the extent of the lesions. In addition, we developed a prognosis prediction model to estimate the short-term severity, defined as intubation or death within one month of CT image acquisition. Our model utilised age, gender and image information, all of which can be extracted from the Digital Imaging and Communications in Medicine (DICOM) image format, enabling conveni- ent deployment in a clinical setting. Its performance was validated in the context of an international challenge and proved robust through evaluation on different, multicentre datasets. In this work, we developed tools for CAD and CDS systems that can help in the shift towards precision medicine. We demonstrated their potential in oncological care for malignant melanoma and in the management of COVID-19.