LAUSR

This paper is concerned with the role of science and technology in helping to create change in society. Diagnostic radiology is an example of an activity that has undergone significant change due to such developments, which over the past 40 years have led to a huge increase in the volume of medical imaging data generated. However, these developments have by and large left the human elements of the radiological process (referrer, radiographer and radiologist) intact. Diagnostic radiology has now reached a stage whereby the volume of information generated cannot be fully utilised solely by employing human observers to form clinical opinions, a process that has not changed in over 100 years. In order to address this problem, the potential application of Artificial Intelligence (AI) in the form of Deep Learning (DL) techniques to diagnostic radiology indicates that the next technological development phase may already be underway. The paper outlines the historical development of AI techniques, including Machine Learning and DL Neural Networks and discusses how such developments may affect radiological practice over the coming decades. The ongoing growth in the world market for radiological services is potentially a significant driver for change. The application of AI and DL learning techniques will place quantification of diagnostic outcomes at the heart of performance evaluation and quality standards. The effect this might have on the optimisation process will be discussed and in particular the possible need for automation in order to meet more stringent and standardised performance requirements that might result from these developments. Changes in radiological practices would also impact upon patient protection including the associated scientific support requirements and these are discussed.