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Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

This article examines decision-making in Federal Court of Canada immigration law applications for stays of removal, focusing on how the rates at which stays are granted depend on which justice decides the case. The article deploys a form of computational natural language processing, using a large-language model machine learning process (GPT-3) to extract data from online Federal Court dockets. The article reviews patterns in outcomes in thousands of stay of removal applications identified through this process and reveals a wide range in stay grant rates across many justices. The article argues that the Federal Court should take measures to encourage more consistency in stay decision-making and cautions against relying heavily on stays of removal to ensure that deportation complies with constitutional procedural justice protections. The article is also a demonstration of how machine learning can be used to pursue empirical legal research projects that would have been cost prohibitive or technically challenging only a few years ago--and shows how technology that is increasingly used to enhance the power of the state at the expense of marginalized migrants can instead be used to scrutinize legal decision-making in the immigration law field, hopefully in ways that enhance the rights of migrants. The article also contributes to the broader field of computational legal research in Canada by making available to other non-commercial researchers the code used for the project, as well as a dataset of several thousand Federal Court dockets that can be used for future research. [ABSTRACT FROM AUTHOR]