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Solving the explainable AI conundrum by bridging clinicians' needs and developers' goals.

Nadine Bienefeld1, Jens Michael Boss2, Rahel Lüthy3

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Developers and clinicians have differing views on explainable AI (XAI) in healthcare, impacting clinical decision support systems. Addressing these differences is key to effective XAI implementation.

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Area of Science:

  • Healthcare Informatics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Explainable artificial intelligence (XAI) is crucial for AI/ML adoption in healthcare.
  • Understanding developer and clinician perspectives on XAI is limited.
  • Conflicting goals and requirements can hinder XAI implementation.

Purpose of the Study:

  • To investigate the differing mental models of developers and clinicians regarding XAI.
  • To identify conflicting goals and requirements in XAI co-design for healthcare.
  • To propose design solutions for effective XAI in clinical decision support systems.

Main Methods:

  • Longitudinal multi-method study.
  • Co-design of an XAI solution for a clinical decision support system.
  • Involved 112 developers and clinicians.

Main Results:

  • Identified three key differences in developer vs. clinician mental models of XAI.
  • Differences include opposing goals (interpretability vs. plausibility), sources of truth (data vs. patient), and knowledge exploration vs. exploitation.
  • Highlighted the need to reconcile these differing perspectives.

Conclusions:

  • Reconciling developer and clinician XAI mental models is vital for healthcare AI.
  • Proposed design solutions: causal inference, personalized explanations, and ambidexterity.
  • Emphasized considering both stakeholder perspectives for effective XAI in healthcare.