J Med Syst
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To support the next generation of healthcare innovators - whether they be engineers, designers, clinicians, or business experts by training - education in the emerging field of medical innovation should be made easily and widely accessible to undergraduate students, graduate students, and young professionals, early in their careers. Currently, medical innovation curricula are taught through semester-long courses or year-long fellowships at a handful of universities, reaching only a limited demographic of participants. This study describes the structure and preliminary outcomes of a 1-2 week "extended hackathon" course that seeks to make medical innovation education and training more accessible and easily adoptable for academic medical centers. ⋯ In this study, the extended hackathon is presented as a novel educational model to teach undergraduate and graduate students a foundational skillset for medical innovation. Participants reported gaining significant knowledge across all ten categories assessed. To more robustly assess the educational value of extended hackathons, a standardized assessment for medical innovation knowledge needs to be developed, and a larger sample size of participants surveyed.
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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. ⋯ This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.