19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Abstract

          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.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: not found
          • Article: not found

          Some Studies in Machine Learning Using the Game of Checkers

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Machine Learning for Medical Imaging.

            Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Deep Learning in Medical Imaging: General Overview

              The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.
                Bookmark

                Author and article information

                Contributors
                +39 3479936904 , filippo.pesapane@unimi.it
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1869-4101
                15 August 2018
                15 August 2018
                October 2018
                : 9
                : 5
                : 745-753
                Affiliations
                [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
                Article
                645
                10.1007/s13244-018-0645-y
                6206380
                30112675
                cf5d908e-a6d7-4412-93b0-e5fe52ad1b47
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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.

                History
                : 18 May 2018
                : 18 June 2018
                : 28 June 2018
                Categories
                Review
                Custom metadata
                © The Author(s) 2018

                Radiology & Imaging
                artificial intelligence,legislation,policy,privacy,radiology
                Radiology & Imaging
                artificial intelligence, legislation, policy, privacy, radiology

                Comments

                Comment on this article