Purpose: Coronaviruses (CoV) are single-stranded RNA viruses that transmit from animal species to humans, causing a threat to global health. We aim to summarize common imaging findings of 3 betacoronaviruses (b-CoVs) and the common clinical manifestation, to provide a better understanding of the courses of the disease. Material and methods:The Pubmed and Google Scholar databases were searched for the terms "SARS-CoV" OR "COVID-19" OR "MERS-CoV". Imaging-specific searches included keyword searches for "CT" AND "imaging". Clinical presentation-specific searches included keyword searches for "clinical" AND "manifestation" AND "cardiovascular" OR "neurology" OR "gastrointestinal" OR "hematology". In total, 77 articles were selected for discussion in the current literature review.Results: Human b-CoVs infection presented consistent indications of ground-glass opacities (GGO), consolidation, and interlobular septal thickening. Pleural effusion was also common in all 3 b-CoVs, but it was least present in SARS-CoV-2 infection. Bilateral lung involvement was common to both MERS-CoV and SARS-CoV-2 infection. Cardiovascular, neurological, haematological, and gastrointestinal were common clinical presentations found in patients infected with b-CoVs. Conclusions:The comparison of imaging findings can be applied in clinical practice to distinguish the 3 CoV through different imaging modalities. It is crucial to understand the possible imaging findings and clinical presentations to better understand the course of the disease as well as prepare for future variants.

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Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. Decision tree representations of medical decision-making tools can be generated using diagnostic metrics found in literature and entropy removal can be calculated for these tools. This analysis was done for 623 diagnostic tools and provided unique insights into the utility of such tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as the quantification of the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data in order to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel thorough evaluation of medical diagnostic algorithms.

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