Purpose: Machine learning (ML) and deep learning (DL) can be utilized in radiology to help diagnosis and for predicting management and outcomes based on certain image findings. DL utilizes convolutional neural networks (CNN) and may be used to classify imaging features. The objective of this literature review is to summarize recent publications highlighting the key ways in which ML and DL may be applied in radiology, along with solutions to the problems that this implementation may face. Material and methods:Twenty-one publications were selected from the primary literature through a PubMed search.The articles included in our review studied a range of applications of artificial intelligence in radiology. Results:The implementation of artificial intelligence in diagnostic and interventional radiology may improve image analysis, aid in diagnosis, as well as suggest appropriate interventions, clinical predictive modelling, and trainee education. Potential challenges include ethical concerns and the need for appropriate datasets with accurate labels and large sample sizes to train from. Additionally, the training data should be representative of the population to which the future ML platform will be applicable. Finally, machines do not disclose a statistical rationale when expounding on the task purpose, making them difficult to apply in medical imaging. Conclusions:As radiologists report increased workload, utilization of artificial intelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or replacing, radiologists. Further research should be done on the risks of AI implementation and how to most accurately validate the results.

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X-rays are perceived by the dark-adapted eye. Elsewhere (Sorsby & O’Connor 1945) an account has been given of the steps leading up to the use of X-rays for measuring the axial length of the eye. The initial work of Rushton (1938) and of Goldmann & Hagen (1942) established this procedure clinically, whilst Stenstrom (1946) has recorded measurement of the axial length in 1000 eyes. Goldmann & Hagen (1942) showed that the total refraction of the eye could be determined directly, and Sorsby & O’Connor (1945) demonstrated th at all the diameters of the globe could be measured. A review of the earlier observations has been given by Hartridge (1946).

In Part 1 by Mr J. Shapiro, the merits of the chosen combined shielding design are explained. The geometry of the radiation head is given including the source movement mechanism. The problem, basic principle and engineering embodiment of the radiation beam definition and control mechanism are discussed. The fundamental approach and engineering solutions of the radiation head alignment system are described. The remote control of the unit and the treatment sequence are briefly reviewed. In Part 2 by Dr A. D. O'Connor, the radioactive isotopes suitable for use in large treatment units in radiotherapy are reviewed with special reference to cobalt 60. In comparison with X-ray generators of such quality, such units are more reliable. The features of design of a unit desirable for isotope teletherapy are set out. The methods of treatment planning and execution are described and the advantages obtained from the Servotec Canterbury unit enumerated.