Jusletter IT

Machine learning in medical diagnostics – inadequacy of existing legal regimes

  • Author: Julianna Chan Lok Yin
  • Category: Articles
  • Region: China
  • Field of law: E-Health, AI & Law
  • Collection: Conference proceedings IRIS 2019
  • Citation: Julianna Chan Lok Yin, Machine learning in medical diagnostics – inadequacy of existing legal regimes, in: Jusletter IT 21. February 2019
Machine learning and artificial intelligence encompasses huge potentials and benefits to thsociety as a whole, especially in the healthcare industry. However, with such potentials also come the question of allocation of liability where things go wrong. As we continue to venture into the unchartered territories of machine learning technologies in healthcare, the urgency to create a suitable regulatory regime becomes every so pressing. This paper analyses the non-suitability of existing legal regimes in regulating machine learning advancements in the healthcare sector, specifically in terms of the attribution of responsibilities when incorrect decisions are made using such technologies. It deals with four main issues: (1) the incompatibility between the tort of negligence and the nature of machine learning;(2) the possibility of regulating artificial intelligence advancements by imposing strict liability; (3) issues anticipated when trying to subject self-learning machines to a legal regime designed for human; and (4) potential options in regulating machine learning technologies in the healthcare sector.

Table of contents

  • 1. Regulatory Landscape of Machine Learning in Healthcare
  • 2. The Role of Machine Learning in Medical Diagnostic
  • 3. The Incompatibility between Tort of Negligence and Machine Learning
  • 4. Possibility of Imposing Strict Liability
  • 5. Potential Options
  • 6. References

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