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Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process.

Benjamin X Collins1,2,3, Jean-Christophe Bélisle-Pipon4, Barbara J Evans5,6

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A new AI lifecycle framework systematically identifies ethical issues in healthcare AI development and use. This approach helps stakeholders deliberate on benefits and harms, preventing adverse outcomes.

Keywords:
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Area of Science:

  • Healthcare AI Ethics
  • Artificial Intelligence Lifecycle
  • Ethical Analysis Frameworks

Background:

  • Artificial intelligence (AI) development involves iterative refinement, presenting unique ethical challenges.
  • Stakeholder perceptions of AI ethics vary, potentially leading to overlooked adverse outcomes.
  • Systematic identification of ethical issues throughout the AI lifecycle is crucial for informed deliberation.

Purpose of the Study:

  • To develop a comprehensive AI lifecycle model for analyzing ethical issues in healthcare.
  • To identify potential benefits, harms, conflicts, and errors at each stage of the AI lifecycle.
  • To illustrate the application of this lifecycle-informed approach through case studies.

Main Methods:

  • Literature review of existing AI lifecycles and ethical issues in healthcare.
  • Consolidation of themes into a novel, comprehensive AI lifecycle.
  • Analysis of AI benefits and harms through the proposed lifecycle to identify ethical questions and potential conflicts.
  • Case study illustrations of ethical dilemmas at different lifecycle stages.

Main Results:

  • A systematic, lifecycle-informed approach to AI ethics analysis enables mapping of AI effects.
  • Case studies demonstrate how ethical dilemmas emerge at various points in the AI lifecycle.
  • The approach facilitates guided deliberations on AI benefits and harms.

Conclusions:

  • The proposed AI lifecycle provides a structured method for ethical analysis in healthcare.
  • This framework enhances communication and deliberation among diverse stakeholders, including patients and healthcare professionals.
  • It supports proactive identification and mitigation of ethical risks associated with AI in healthcare.