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Artificial intelligence in clinical decision-making: Rethinking personal moral responsibility.

Helen Smith1, Giles Birchley1, Jonathan Ives1

  • 1Centre for Ethics in Medicine, University of Bristol Medical School, Bristol, UK.

Bioethics
|September 19, 2023
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Summary
This summary is machine-generated.

Software developing companies (SDCs) create artificial intelligence systems (AISs) for clinical decisions. This paper argues that both clinicians and SDCs should share moral responsibility for AIS outcomes, especially when foreseeability is involved.

Keywords:
artificial intelligencebioethicsclinical decision-makingclinicianethicsresponsibility

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Area of Science:

  • Medical Ethics
  • Artificial Intelligence in Healthcare
  • Software Development

Background:

  • Clinicians traditionally lead healthcare decisions.
  • Software developing companies (SDCs) are introducing artificial intelligence systems (AISs) to influence clinical decision-making.
  • SDCs state clinicians remain the final decision-makers, with AISs only informing choices.

Purpose of the Study:

  • To examine the attribution of personal moral responsibility for outcomes involving AIS in clinical decision-making.
  • To challenge the default position that clinicians solely hold responsibility.
  • To propose a framework for just responsibility attribution.

Main Methods:

  • Analysis of prospective versus retrospective responsibility.
  • Examination of foreseeability as a key factor in attributing moral responsibility.
  • Ethical examination of SDC and clinician roles in AIS use.

Main Results:

  • The current default position of clinician-only responsibility is imbalanced and unjust.
  • Foreseeability is crucial for justly attributing moral responsibility.
  • AISs influence clinician judgment, complicating sole clinician accountability.

Conclusions:

  • Moral responsibility for AIS outcomes in healthcare should be shared between clinical users and SDCs.
  • A nuanced approach considering foreseeability is necessary for ethical AIS implementation.
  • This shared responsibility model promotes a more just and balanced approach to AI in clinical settings.