Responsible and Regulatory Conform Machine Learning for Medicine: a Survey of Technical Challenges and Solutions
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Technical Challenges and Solutions Eike Petersen*1, Yannik Potdevin2, Esfandiar Mohammadi1, Stephan Zidowitz3, Sabrina Breyer1, Dirk Nowotka2, Sandra Henn1, Ludwig Pechmann4, Martin Leucker1,4, Philipp Rostalski1,5, and Christian Herzog1 1Universität zu Lübeck, Lübeck, Germany 2Christian-Albrechts-Universität zu Kiel, Kiel, Germany 3Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany 4UniTransferKlinik Lübeck, Lübeck, Germany 5Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany July 19, 2021 Abstract Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of harm, justice, privacy, and transparency are respected, medical machine learning applications must be developed responsibly. A large number of high-level declarations of ethical principles have been put forth for this purpose, but there is a severe lack of technical guidelines explicating the practical consequences for medical machine learning. Similarly, there is currently consid- erable uncertainty regarding the exact regulatory requirements placed upon medical machine learning systems. In this paper, we survey the technical challenges involved in creating medi- cal machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges. We begin by providing a brief overview of existing regulations affecting medical machine learning, showing that properties such as safety, robustness, reliability, privacy, security, transparency, explainability, and nondiscrim- ination are all demanded already by existing law and regulations — albeit, in many cases, to an uncertain degree. Next, we discuss the key technical obstacles to achieving these desir- able properties, and important techniques to overcome those barriers in the medical context.
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