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      Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States

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          Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable.

          Teaching Points

          AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness.

          Regulations for safety, privacy protection, and ethical use of sensitive information are needed.

          EU and U.S. have different approaches for approving and regulating new medical devices.

          EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity.

          U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

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          Most cited references 40

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Human-level control through deep reinforcement learning.

            The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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              Some Studies in Machine Learning Using the Game of Checkers

               A. L. Samuel (1959)

                Author and article information

                +39 3479936904 ,
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                15 August 2018
                15 August 2018
                October 2018
                : 9
                : 5
                : 745-753
                [1 ]ISNI 0000 0004 1757 2822, GRID grid.4708.b, Postgraduation School in Radiodiagnostics, , Università degli Studi di Milano, ; Via Festa del Perdono 7, 20122 Milan, Italy
                [2 ]Independent Researcher, 3 Greenwich Court, Cavell Street, London, E1 2BS UK
                [3 ]ISNI 0000 0004 1766 7370, GRID grid.419557.b, Unit of Radiology, , IRCCS Policlinico San Donato, ; Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
                [4 ]ISNI 0000 0004 1757 2822, GRID grid.4708.b, Department of Biomedical Sciences for Health, , Università degli Studi di Milano, ; Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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                © The Author(s) 2018

                Radiology & Imaging

                radiology, privacy, policy, legislation, artificial intelligence


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