To investigate the application status of emergency radiology in China, and to provide data support for the standardized development, scientific management and big data research of emergency radiology.

From August 12th to October 19th, 2022, a questionnaire survey was conducted through WeChat"Questionnaire Star"to send targeted questionnaires to investigate the relevant data of the current status of emergency radiology in China, mainly including digital radiography (DR) and computed tomography (CT). This study was initiated by the Chinese Emergency Radiology Database Collaboration Group, and comprehensively investigated emergency imaging personnel, equipment, workload, critical value reporting process, and artificial intelligence (AI) application status.

There were 123 hospitals in the study. The survey showed that emergency DR/CT reports were mainly completed by residents and above (69.1%). There were 21 DR brands, 10 CT brands and 8 MR brands used for emergency imaging examinations. The median number of DR examinations in tertiary hospitals and secondary hospitals investigated from January to June 2022 was 4 642 and 2 015 cases respectively, and the median number of CT examinations was 16 512 and 3 762 cases respectively. The average single-shift workload of DR in the emergency radiology department during the day and night shift in tertiary hospitals was mainly ≤20 copies and 21-50 copies, and the average single-shift workload of CT in the emergency radiology department during the day and night shift was mainly 21-50 copies and 51-100 copies, while the average single-shift workload of DR/CT in the emergency radiology department during the day/night shift in secondary hospitals was mainly ≤20 copies. In terms of critical value reporting process, 74.8% of emergency imaging doctors and 84.6% of emergency imaging technicians took the way of phone/text message to notify the clinical doctor or the patients′ family. The overall deployment rate of AI in emergency imaging was about 60.2%. 75% of the respondents believed that in the future, AI can improve emergency radiology work from aspects such as emergency screening, aided diagnosis and process optimization.

The emergency medical imaging mainly based on DR and CT has the current situations such as generally low seniority of doctors, diverse brands of imaging equipments, large volume of examinations and intense workload per doctor, especially in tertiary hospitals, and dependence on traditional means for critical value reporting. At present, AI is emerging in the field of emergency imaging, and there is still a long way to go to play the huge potential of AI in the intelligent whole process of emergency imaging in the future.

Emergency treatment; Medical imaging; Survey report; Digital radiography; Tomography, X-ray computed