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Understanding, explaining, and utilizing medical artificial intelligence

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Listed:
  • Romain Cadario

    (Erasmus University)

  • Chiara Longoni

    (Boston University)

  • Carey K. Morewedge

    (Boston University)

Abstract

Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a ‘black box’) and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1–3B: N = 2,699), we find that people exhibit an illusory understanding of human medical decision-making (study 1). This leads people to believe they better understand decisions made by human than algorithmic healthcare providers (studies 2A,B), which makes them more reluctant to utilize algorithmic than human providers (studies 3A,B). Fortunately, brief interventions that increase subjective understanding of algorithmic decision processes increase willingness to utilize algorithmic healthcare providers (studies 3A,B). A sixth study on Google Ads for an algorithmic skin cancer detection app finds that the effectiveness of such interventions generalizes to field settings (study 4: N = 14,013).

Suggested Citation

  • Romain Cadario & Chiara Longoni & Carey K. Morewedge, 2021. "Understanding, explaining, and utilizing medical artificial intelligence," Nature Human Behaviour, Nature, vol. 5(12), pages 1636-1642, December.
    • Handle: RePEc:nat:nathum:v:5:y:2021:i:12:d:10.1038_s41562-021-01146-0
    DOI: 10.1038/s41562-021-01146-0
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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    Cited by:

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    2. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
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    5. Erik Hermann & Gizem Yalcin Williams & Stefano Puntoni, 2024. "Deploying artificial intelligence in services to AID vulnerable consumers," Journal of the Academy of Marketing Science, Springer, vol. 52(5), pages 1431-1451, October.
    6. Yang, Yikai & Zheng, Jiehui & Yu, Yining & Qiu, Yiling & Wang, Lei, 2024. "The role of recommendation sources and attribute framing in online product recommendations," Journal of Business Research, Elsevier, vol. 174(C).
    7. Anne-Marie Nussberger & Lan Luo & L. Elisa Celis & M. J. Crockett, 2022. "Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Wang, Cuicui & Li, Yiyang & Fu, Weizhong & Jin, Jia, 2023. "Whether to trust chatbots: Applying the event-related approach to understand consumers’ emotional experiences in interactions with chatbots in e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    9. Huang, Xiaozhi & Wu, Xitong & Cao, Xin & Wu, Jifei, 2023. "The effect of medical artificial intelligence innovation locus on consumer adoption of new products," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    10. Hai Lan & Xiaofei Tang & Yong Ye & Huiqin Zhang, 2024. "Abstract or concrete? The effects of language style and service context on continuous usage intention for AI voice assistants," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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