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This summary is machine-generated.

This study proposes viewing patients as "fellow workers" and epistemic partners for ethical AI in healthcare. It suggests integrating ethical principles rather than weighing them for evaluating machine learning-driven decision support systems.

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Area of Science:

  • Medical Ethics
  • Artificial Intelligence in Healthcare
  • Machine Learning Ethics

Background:

  • Ethical legitimacy of Machine Learning-driven decision support systems (ML_CDSS) often relies on concepts like "human in the loop" or "meaningful human control."
  • Ethical principles are typically used as a reference in guidance documents, requiring the balancing of conflicting principles.

Purpose of the Study:

  • To propose a novel interpretation of the "human in the loop" concept for ML_CDSS.
  • To offer a framework for evaluating AI in healthcare that moves beyond the balancing of competing ethical principles.
  • To reframe the patient's role in the context of AI-driven healthcare decisions.

Main Methods:

  • Adopting a neo-Kantian philosophical perspective, drawing on the work of Onora O'Neill.
  • Analyzing the role of the patient in interpreting outputs from ML_CDSS.
  • Developing an alternative approach to integrating ethical principles in AI evaluation.

Main Results:

  • Patients should be considered "fellow workers" and epistemic partners in understanding ML_CDSS outputs.
  • An integration-based approach to ethical principles is more appropriate for medical AI evaluation than a balancing approach.
  • This reframing supports ethical legitimacy by emphasizing collaborative interpretation.

Conclusions:

  • The "human in the loop" in healthcare AI should be understood as an active, epistemic partner.
  • Integrating ethical principles offers a more robust method for evaluating medical AI than traditional balancing acts.
  • This approach enhances the ethical legitimacy of AI in healthcare by fostering shared understanding and decision-making.