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<b>PURPOSE:</b> To implement the technical feasibility of an AI-based software prototype optimized for the detection of COVID-19 pneumonia in CT datasets of the lung and the differentiation between other etiologies of pneumonia. <b>METHODS:</b> This single-center retrospective case–control-study consecutively yielded 144 patients (58 female, mean age 57.72 ± 18.25 y) with CT datasets of the lung. Subgroups including confirmed bacterial (<i>n</i> = 24, 16.6%), viral (<i>n</i> = 52, 36.1%), or fungal (<i>n</i> = 25, 16.6%) pneumonia and (<i>n</i> = 43, 30.7%) patients without detected pneumonia (comparison group) were evaluated using the AI-based <i>Pneumonia Analysis prototype</i>. Scoring (extent, etiology) was compared to reader assessment. <b>RESULTS:</b> The software achieved an optimal sensitivity of 80.8% with a specificity of 50% for the detection of COVID-19; however, the human radiologist achieved optimal sensitivity of 80.8% and a specificity of 97.2%. The mean postprocessing time was 7.61 ± 4.22 min. The use of a contrast agent did not influence the results of the software (<i>p</i> = 0.81). The mean evaluated COVID-19 probability is 0.80 ± 0.36 significantly higher in COVID-19 patients than in patients with fungal pneumonia (<i>p</i> < 0.05) and bacterial pneumonia (<i>p</i> < 0.001). The mean percentage of opacity (PO) and percentage of high opacity (PHO ≥ −200 HU) were significantly higher in COVID-19 patients than in healthy patients. However, the total mean HU in COVID-19 patients was −679.57 ± 112.72, which is significantly higher than in the healthy control group (<i>p</i> < 0.001). <b>CONCLUSION:</b> The detection and quantification of pneumonia beyond the primarily trained COVID-19 datasets is possible and shows comparable results for COVID-19 pneumonia to an experienced reader. The advantages are the fast, automated segmentation and quantification of the pneumonia foci.