Understanding, explaining, and utilizing medical artificial intelligence

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 percept...

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Bibliographic Details
Published in:Nature human behaviour 2021-12, Vol.5 (12), p.1636-1642
Main Authors: Cadario, Romain, Longoni, Chiara, Morewedge, Carey K.
Format: Article
Language:English
Subjects:
4014/4001
4014/477/2811
Adult
Algorithms
Artificial Intelligence
Behavioral Sciences
Biomedical and Life Sciences
Brief interventions
Clinical Decision-Making
Cost analysis
Decision making
Delivery of Health Care
Experimental Psychology
Female
Health care
Humans
Intelligence
Life Sciences
Male
Medical decision making
Microeconomics
Middle Aged
Neurosciences
Personality and Social Psychology
Resistance
Skin cancer
Understanding
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Summary: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). Cadario et al. identify potential reasons underlying the resistance to use medical artificial intelligence and test interventions to overcome this resistance.
ISSN:2397-3374
2397-3374
DOI:10.1038/s41562-021-01146-0