Diagnostic radiology is a rapidly evolving speciality, with advances in technology leading to new innovations in scanner hardware and image processing in the last decade. Artificial intelligence (AI) has been most impactful in health care to date in the radiology field, due to the digitalisation of radiology, and the increasing demands on radiologists due to increasing workload.
AI is commonly used in all aspects of diagnostic radiology. It is used to help choose the most appropriate imaging tests, improve patient booking systems, and other non clinical use cases. Clinically, it is increasingly used to improve radiologist accuracy, identify cases with urgent time sensitive pathology, enhance image quality, and to reduce both radiation dose and study acquisition time. AI is being used to improve clinical outcomes for patients, as well as address issues with access to services and radiologist resourcing.
The current uses of AI in diagnostic radiology will be presented here, as well as the potential use cases for AI in diagnostic radiology in the years to come. Several key use cases such as stroke imaging and thoracic imaging will be discussed.
