A deep learning AI-based application, Milvue Suite, can identify multiple relevant and critical findings on emergency chest radiographs, with a high level of accuracy compared to expert thoracic radiologists, mostly when identifying critical conditions such as pneumothorax and fractures. Overall, the high negative predictive value underscores the algorithm's potential as a reliable tool for the triage of chest radiographs in emergency department settings.

However, it is important to acknowledge the limitations of this study, including the relatively small dataset size, which may impact the generalizability of the findings. Additionally, the observed false positive rate for relevant findings indicates that while AI can significantly aid in the diagnostic process, the need for specialized radiological review remains essential to avoid unnecessary further investigations and ensure optimal patient management.

The integration of AI in the analysis of chest radiographs represents a significant step forward in addressing the challenges faced by emergency medicine, including the high volume of radiographs, the need for rapid and accurate interpretation, and the limitations posed by the availability of radiological expertise, particularly during off-hours. The findings of this study are concordant with previous studies evaluating the Milvue Suite solution on chest radiographs and suggest that the Milvue Suite can play a crucial role in enhancing diagnostic processes, thereby improving patient management and care outcomes.

Future studies in larger prospective cohorts are needed. Moreover, an analysis of the impact of the device on physicians’ diagnosis (radiologists, emergency physicians, residents) may provide more insight into the advantage of this software.