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Cybersecurity of AI medical devices: risks, legislation, and challenges

arXiv 2303.03140 ↗First posted 6 Mar 2023cs.CR cs.AI cs.CYcited by 0

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Abstract (arXiv)

Medical devices and artificial intelligence systems rapidly transform healthcare provisions. At the same time, due to their nature, AI in or as medical devices might get exposed to cyberattacks, leading to patient safety and security risks. This book chapter is divided into three parts. The first part starts by setting the scene where we explain the role of cybersecurity in healthcare. Then, we briefly define what we refer to when we talk about AI that is considered a medical device by itself or supports one. To illustrate the risks such medical devices pose, we provide three examples: the poisoning of datasets, social engineering, and data or source code extraction. In the second part, the paper provides an overview of the European Union's regulatory framework relevant for ensuring the cybersecurity of AI as or in medical devices (MDR, NIS Directive, Cybersecurity Act, GDPR, the AI Act proposal and the NIS 2 Directive proposal). Finally, the third part of the paper examines possible challenges stemming from the EU regulatory framework. In particular, we look toward the challenges deriving from the two legislative proposals and their interaction with the existing legislation concerning AI medical devices' cybersecurity. They are structured as answers to the following questions: (1) how will the AI Act interact with the MDR regarding the cybersecurity and safety requirements?; (2) how should we interpret incident notification requirements from the NIS 2 Directive proposal and MDR?; and (3) what are the consequences of the evolving term of critical infrastructures? [This is a draft chapter. The final version will be available in Research Handbook on Health, AI and the Law edited by Barry Solaiman & I. Glenn Cohen, forthcoming 2023, Edward Elgar Publishing Ltd]

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 51 references that are in the map. First posted on arXiv 6 Mar 2023.

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References · 5

  1. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  2. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  3. 2021Removing Adversarial Noise in Class Activation Feature Spacecited by 18
  4. 2021Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultscited by 4
  5. 2020Dissecting liabilities in adversarial surgical robot failures: A national (Danish) and European law perspectivecited by 2

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