Publications by authors named "Rita Matulionyte"

One of the challenges of AI technologies is its "black box" nature, or the lack of explainability and interpretability of these technologies. This chapter explores whether AI systems in healthcare generally, and in neurosurgery specifically, should be explainable, for what purposes, and whether the current XAI ("explainable AI") approaches and techniques are able to achieve these purposes. The chapter concludes that XAI techniques, at least currently, are not the only and not necessarily the best way to achieve trust in AI and ensure patient autonomy or improved clinical decision, and they are of limited significance in determining liability.

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Article Synopsis
  • Computational neurosurgery combines artificial intelligence and computational modeling to enhance the diagnosis and treatment of neurosurgical conditions, aiming to advance clinical neurosciences.
  • The field seeks to integrate ethical considerations to ensure that the use of AI is conducted responsibly and prioritizes patient care, ultimately aiming to prevent errors in treatment.
  • This initiative serves as a guide for practitioners, ethicists, and scientists in the application of ethical standards within computational neurosurgery.
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The introduction of novel medical technology, such as artificial intelligence (AI), into traditional clinical practice presents legal liability challenges that need to be squarely addressed by litigants and courts when something goes wrong. Some of the most promising applications for the use of AI in medicine will lead to vexed liability questions. As AI in health care is in its relative infancy, there is a paucity of case law globally upon which to draw.

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