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Purpose: To assess the incidence of erroneous diagnosis of pneumatosis (pseudo-pneumatosis) in patients who underwent an emergency abdominal CT and to verify the performance of imaging features, supported by artificial intelligence (AI) techniques, to reduce this misinterpretation. Methods: We selected 71 radiological reports where the presence of pneumatosis was considered definitive or suspected. Surgical findings, clinical outcomes, and reevaluation of the CT scans were used to assess the correct diagnosis of pneumatosis. We identified four imaging signs from literature, to differentiate pneumatosis from pseudo-pneumatosis: gas location, dissecting gas in the bowel wall, a circumferential gas pattern, and intramural gas beyond a gas-fluid/faecal level. Two radiologists reevaluated in consensus all the CT scans, assessing the four above-mentioned variables. Variable discriminative importance was assessed using the Fisher exact test. Accurate and statistically significant variables (p-value < 0.05, accuracy > 75%) were pooled using boosted Random Forests (RFs) executed using a Leave-One-Out cross-validation (LOO cv) strategy to obtain unbiased estimates of individual variable importance by permutation analysis. After the LOO cv, the comparison of the variable importance distribution was validated by one-sided Wilcoxon test. Results: Twenty-seven patients proved to have pseudo-pneumatosis (error: 38%). The most significant features to diagnose pneumatosis were presence of dissecting gas in the bowel wall (accuracy: 94%), presence of intramural gas beyond a gas-fluid/faecal level (accuracy: 86%), and a circumferential gas pattern (accuracy: 78%). Conclusion: The incidence of pseudo-pneumatosis can be high. The use of a checklist which includes three imaging signs can be useful to reduce this overestimation. [ABSTRACT FROM AUTHOR]