Mater Radiology first to use AI on scans

The Mater Misericordiae University Hospital has become the first hospital in Ireland to use artificial intelligence (AI) across its Radiology Department in order to accelerate emergency care. This has resulted in rapid notification of suspected pathologies, enhanced patient care and is freeing up valuable time for staff in the department.

Since the introduction of the AI-assisted software this year, more than 15,600 patient scans have been analysed. More than 700 pathologies were correctly flagged by AI within two to three minutes of the scan being completed, with 500 intracranial haemorrhages and 200 pulmonary emboli being identified.

On outpatient scans, 50 pulmonary emboli were also flagged where the scans were being performed to assess other medical conditions. The overall accuracy of the software in real Irish clinical practice has been deemed to be highly sensitive and specific, with an accuracy rate of more than 90%.

The Mater Hospital has implemented four new technologies, including AI assistance on: CT pulmonary angiograms; incidental pulmonary embolism notification on CT scans; intracranial haemorrhage detection on CT head scans; and cervical spine fracture detection on CT C-Spine scans. 

The screening platform, made by Aidoc, operates on an ‘always-on’ basis, meaning it is constantly running in the background in order to analyse medical imaging data, flag urgent findings, and highlight cases that should be prioritised and brought to the immediate attention of consultants. The use of the software in Radiology at the Mater has significantly reduced report turnaround times, while also improving diagnostic accuracy and streamlining workflows. 

Prof Peter MacMahon, consultant radiologist at the Mater Hospital, said: “Our experiences have underscored the tangible benefits of AI, notably in expediting critical diagnoses and reducing turnaround times by rapidly flagging anomalies detected in scans.”

Early this year, the Mater Hospital plans to trial further specialised AI tools, focusing on the analysis of bone and chest x-rays in the Emergency Department.