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radiology and imaging Artificial intelligence and radiology Artificial intelligence enables content aggregation that extracts information from diverse healthcare data silos to help radiologists create actionable imaging reports Neelam Dugar MD Consultant Radiologist & Clinical PACS Lead Doncaster & Bassetlaw Hospitals NHS Foundation Trust, UK In 2016 Geoff Hinton, the British cognitive psychologist and father of artificial intelligence (AI) and machine learning, caught the attention of politicians by declaring that artificial intelligence, and machine learning, would replace radiologists. He could not have been further from the truth. It is clear from the recent presentations at ECR and RSNA that radiologists will become smarter and more efficient with the help of AI, but will not be replaced by it. Radiologists have been using AI for a long time now. Most radiology departments in the National Health Service (NHS) use voice recognition technology – which uses computer audition AI. The success of voice recognition technology is down to the integration with radiologists’ image reading workflow. These are many types of computer vision AI that are being assessed for use in radiology including: • Computer-aided anomaly detection – AI 166 HHE 2018 | hospitalhealthcare.com would be used for detection of abnormalities such as breast lesions, lung nodules on CT, lung shadows on chest X-ray, stroke on CT, haemorrhage on CT, fracture on plain X-ray, colonic polyp, pulmonary embolism detection on CT, etc • Computer-aided simple triage – when an anomaly is detected, AI could be used to raise the priority of reporting in the worklist. This is being used for CT detection of haemorrhage in the head and prioritises reporting • Computer-aided change detection – this form of AI would allow detection of change on serial studies, for example, multiple sclerosis, tumour progression, etc • Image fusion or co-registration – these machine learning algorithms allow fusion of images between different modalities, for example, CT and MRI, etc • Computer-aided classification (also called