As healthcare systems face growing pressure to expand diagnostic capacity and reduce waiting times, new research suggests that Artificial Intelligence could help improve efficiency at the point of imaging.
For radiology teams, this pressure is felt day-to-day. Radiographers are being asked to deliver more scans with the same resources, often managing increasingly complex workflows under time constraints.
Against this backdrop, much of the focus on AI in radiology has been on image interpretation. However, new findings published in the Journal of Medical Imaging and Radiation Sciences highlight its potential to deliver impact earlier in the process, during image acquisition.
As part of a study evaluating the impact of AI on CT scan time and workflow efficiency, a joint scientific collaboration between Canon and the Royal Bournemouth Hospital compared two CT systems. This included the AI-assisted INSTINX platform on the Aquilion ONE INSIGHT Edition and a non-AI-assisted platform on the Aquilion ONE GENESIS Edition. The results showed that the AI-assisted system delivered scan time reductions of up to 53% and a 45% decrease in user interactions.
The study evaluated four routine CT protocols, with 12 certified diagnostic radiographers performing scans on both systems using a standardised setup. Protocols included brain, chest, abdomen and pelvis imaging, and in each case, delivered faster acquisition times and required fewer manual inputs using the AI-assisted platform.
That’s because each stage of the traditional scanning workflow involves a series of manual steps, from patient positioning to selecting scan parameters, adding time and complexity for radiographers. The AI-assisted platform, instead, uses technologies such as the 3D Landmark Scan and Anatomical Landmark Detection to support automated scan planning and positioning. This helps define the correct scan range and field of view, reducing the need for manual adjustments and streamlining the overall process. Previous studies have also shown that these types of automated approaches can reduce over-scanning in routine chest, abdomen and pelvis imaging, helping to lower unnecessary radiation dose for patients.
For radiographers, fewer interactions with the system can also reduce time spent managing controls, allowing more focus on patient care. The study also found no significant correlation between radiographer experience and performance outcomes, suggesting the technology may support more consistent workflows across teams with varying levels of expertise.
Huw Jones, CT Business Manager at Canon Medical Systems UK, said: “Radiology teams are under constant pressure to do more with limited resources. If you can reduce the time and number of steps required for each scan, you’re not just improving efficiency, you are creating more time to focus on patient care, therefore improving patient experience. Over time, even small-time savings per scan can translate into meaningful operational gains.”
Together, the findings show how AI-assisted acquisition platforms can deliver measurable improvements in CT workflow, helping departments increase capacity while maintaining consistency in everyday practice. As imaging demand continues to grow, solutions that streamline and speed up scanning are likely to play an important role in supporting both clinical teams and patient pathways.
