Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Gadolinium-based contrast agents are used for enhancement during magnetic resonance imaging (MRI). Safety concerns have emerged over retained gadolinium in the globus pallidi. 1,2 Neurotoxic effects have been seen in animals and when gadolinium is given intrathecally in humans. 1 In July 2015, the US Food and Drug Administration stated that it was unknown whether gadolinium deposits were harmful. The substantia nigra (affected in Parkinson disease) directs voluntary movement via signals to the globus pallidi. Consequences of damage to the globus pallidi may include parkinsonian symptoms. 3 We conducted a population-based study to assess the association between gadolinium exposure and parkinsonism.Methods | Sunnybrook Hospital granted ethics approval and deemed the study exempt from participant consent. Multiple linked administrative databases from Ontario, Canada, were used. The population has universal health care, and medication coverage is provided for those older than 65 years. Using fee codes submitted by radiologists, all patients older than 66 years who underwent an initial MRI between April 2003 and March 2013 were identified. Patients whose

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Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one cardiac phase, is even more challenging since the uncertain relatedness intra and inter each type of indices may hinder the learning procedure from better convergence and generalization. In this paper, we propose a newly-designed multitask learning network (FullLVNet), which is constituted by a deep convolution neural network (CNN) for expressive feature embedding of cardiac structure; two followed parallel recurrent neural network (RNN) modules for temporal dynamic modeling; and four linear models for the final estimation. During the final estimation, both intra-and inter-task relatedness are modeled to enforce improvement of generalization: 1) respecting intra-task relatedness, group lasso is applied to each of the regression tasks for sparse and common feature selection and consistent prediction; 2) respecting inter-task relatedness, three phase-guided constraints are proposed to penalize violation of the temporal behavior of the obtained LV indices. Experiments on MR sequences of 145 subjects show that FullLVNet achieves high accurate prediction with our intra-and inter-task relatedness, leading to MAE of 190mm 2 , 1.41mm, 2.68mm for average areas, RWT, dimensions and error rate of 10.4% for the phase classification. This endows our method a great potential in comprehensive clinical assessment of global, regional and dynamic cardiac function.

Bowel sonography for inflammatory bowel disease can be performed in low-volume centers and provides diagnostic accuracy for luminal disease comparable with published data, although it is less sensitive for complications of Crohn disease.