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The acceptance of artificial intelligence (AI) systems by health professionals is crucial to obtain a positive impact on the diagnosis pathway. We evaluated user satisfaction with an AI system for the automated detection of findings in chest x-rays, after five months of use at the Emergency Department. We collected quantitative and qualitative data to analyze the main aspects of user satisfaction, following the Technology Acceptance Model. We selected the intended users of the system as study participants: radiology residents and emergency physicians. We found that both groups of users shared a high satisfaction with the system’s ease of use, while their perception of output quality (i.e., diagnostic performance) differed notably. The perceived usefulness of the application yielded positive evaluations, focusing on its utility to confirm that no findings were omitted, and also presenting distinct patterns across the two groups of users. Our results highlight the importance of clearly differentiating the intended users of AI applications in clinical workflows, to enable the design of specific modifications that better suit their particular needs. This study confirmed that measuring user acceptance and recognizing the perception that professionals have of the AI system after daily use can provide important insights for future implementations.

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Chronic follicular cholecystitis (CFC) is a rare pathology characterized by prominent lymphoid follicles in the lamina propria distributed throughout the gallbladder wall. It has also been mentioned in the literature as lymphoid hyperplasia and pseudolymphoma. CFC represents less than 2% of cholecystectomies. Its etiopathology is mostly unknown. Most reports are based on histopathological findings, with little or no imaging analysis. We describe a case involving a 66-year-old man radiologically diagnosed as xanthogranulomatous cholecystitis (XGC) versus malignancy, revealing CFC with surrounding inflammatory changes in the cholecystectomy specimen.

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