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Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review.

Harishankar V Subramanian1, Casey Canfield1, Daniel B Shank2

  • 1Engineering Management & Systems Engineering, Missouri University of Science and Technology, 600 W 14(th) Street, Rolla, MO 65409, United States of America.

Artificial Intelligence in Medicine
|March 10, 2024
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Summary

Explainable AI (XAI) features in healthcare enhance transparency and trust. Stakeholders prefer customizable XAI, balancing ease of use with detailed insights for complex tasks.

Keywords:
Explainable AIHuman-AI teamHuman-centered designKidney transplantStakeholder engagementTrust in AI

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Human-Computer Interaction
  • Medical Informatics

Background:

  • Complex AI systems necessitate Explainable AI (XAI) for transparency.
  • Existing XAI research focuses on user trust and task performance through system- and prediction-level features.

Purpose of the Study:

  • To analyze stakeholder engagement on AI in kidney transplantation.
  • To identify key themes for designing effective XAI systems.
  • To conduct a scoping literature review on current XAI features.

Main Methods:

  • Nine-month stakeholder engagement process (interviews, workshops) with three groups.
  • Workflow analysis to identify AI intervention points.
  • Assessment of a mock XAI decision support system.
  • Scoping literature review based on identified themes.

Main Results:

  • Four key themes for XAI design: prediction use, information content, personalization, and customization.
  • AI prediction timing (before, during, after decisions) impacts benefits based on task complexity.
  • Experts prefer minimal XAI for simple cases; most stakeholders want optional features for complex cases.
  • Both system- and prediction-level XAI information are crucial for user mental models.
  • XAI can improve trust but not always human-AI team performance.

Conclusions:

  • Stakeholders desire agency over XAI interface for controlling information levels.
  • Future research should focus on customizing XAI features for user preferences and task complexity.
  • Tailored XAI design is essential for effective AI integration in healthcare.