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Diverse patients’ attitudes towards Artificial Intelligence (AI) in diagnosis

Author

Listed:
  • Christopher Robertson
  • Andrew Woods
  • Kelly Bergstrand
  • Jess Findley
  • Cayley Balser
  • Marvin J Slepian

Abstract

Artificial intelligence (AI) has the potential to improve diagnostic accuracy. Yet people are often reluctant to trust automated systems, and some patient populations may be particularly distrusting. We sought to determine how diverse patient populations feel about the use of AI diagnostic tools, and whether framing and informing the choice affects uptake. To construct and pretest our materials, we conducted structured interviews with a diverse set of actual patients. We then conducted a pre-registered (osf.io/9y26x), randomized, blinded survey experiment in factorial design. A survey firm provided n = 2675 responses, oversampling minoritized populations. Clinical vignettes were randomly manipulated in eight variables with two levels each: disease severity (leukemia versus sleep apnea), whether AI is proven more accurate than human specialists, whether the AI clinic is personalized to the patient through listening and/or tailoring, whether the AI clinic avoids racial and/or financial biases, whether the Primary Care Physician (PCP) promises to explain and incorporate the advice, and whether the PCP nudges the patient towards AI as the established, recommended, and easy choice. Our main outcome measure was selection of AI clinic or human physician specialist clinic (binary, “AI uptake”). We found that with weighting representative to the U.S. population, respondents were almost evenly split (52.9% chose human doctor and 47.1% chose AI clinic). In unweighted experimental contrasts of respondents who met pre-registered criteria for engagement, a PCP’s explanation that AI has proven superior accuracy increased uptake (OR = 1.48, CI 1.24–1.77, p

Suggested Citation

  • Christopher Robertson & Andrew Woods & Kelly Bergstrand & Jess Findley & Cayley Balser & Marvin J Slepian, 2023. "Diverse patients’ attitudes towards Artificial Intelligence (AI) in diagnosis," PLOS Digital Health, Public Library of Science, vol. 2(5), pages 1-16, May.
    • Handle: RePEc:plo:pdig00:0000237
    DOI: 10.1371/journal.pdig.0000237
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    References listed on IDEAS

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    1. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    2. Hal R. Arkes & Victoria A. Shaffer & Mitchell A. Medow, 2007. "Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid," Medical Decision Making, , vol. 27(2), pages 189-202, March.
    3. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    4. Chiara Longoni & Andrea Bonezzi & Carey K. Morewedge, 2020. "Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Becksted (2020)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(3), pages 446-448, May.
    5. repec:cup:judgdm:v:15:y:2020:i:3:p:446-448 is not listed on IDEAS
    6. Longoni, Chiara & Bonezzi, Andrea & Morewedge, Carey K., 2020. "Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Beckstead (2020)," Judgment and Decision Making, Cambridge University Press, vol. 15(3), pages 446-448, May.
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