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Understanding, explaining, and utilizing medical artificial intelligence.

Romain Cadario1, Chiara Longoni2, Carey K Morewedge2

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People resist medical artificial intelligence (AI) due to perceived complexity and overconfidence in human doctors. Increasing understanding of AI decision-making boosts user trust and adoption of AI healthcare tools.

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Area of Science:

  • Medical Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Economics

Background:

  • Medical artificial intelligence (AI) offers cost-effectiveness and scalability, often surpassing human performance.
  • Despite AI's potential, public reluctance to utilize these technologies persists.
  • Understanding the drivers of this resistance is crucial for AI adoption in healthcare.

Purpose of the Study:

  • To investigate the psychological factors underlying resistance to medical AI.
  • To examine the role of perceived understanding of decision-making processes (both human and algorithmic) in AI utilization.
  • To test interventions aimed at increasing acceptance of AI in healthcare.

Main Methods:

  • Five pre-registered experiments (N=2,699) assessed understanding of human vs. algorithmic medical decision-making.
  • Studies manipulated perceived understanding and measured willingness to use AI providers.
  • A large-scale field study (N=14,013) evaluated intervention effectiveness via Google Ads for a skin cancer detection app.

Main Results:

  • Participants demonstrated an illusory understanding of human medical decision-making.
  • This illusory understanding led to a preference for human providers over AI, increasing reluctance to use AI.
  • Interventions enhancing the perceived understanding of AI processes significantly increased willingness to utilize AI healthcare providers.
  • Intervention effectiveness was confirmed in a real-world online setting.

Conclusions:

  • Resistance to medical AI stems from a combination of the 'black box' problem and an overestimation of understanding human decision-making.
  • Brief interventions focused on demystifying AI processes can effectively enhance user trust and willingness to adopt AI healthcare solutions.
  • These findings have significant implications for the design and implementation of AI technologies in clinical practice and public health campaigns.