Literature DB >> 34183800

Understanding, explaining, and utilizing medical artificial intelligence.

Romain Cadario1, Chiara Longoni2, Carey K Morewedge2.   

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).
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.

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Year:  2021        PMID: 34183800     DOI: 10.1038/s41562-021-01146-0

Source DB:  PubMed          Journal:  Nat Hum Behav        ISSN: 2397-3374


  27 in total

1.  Can we open the black box of AI?

Authors:  Davide Castelvecchi
Journal:  Nature       Date:  2016-10-06       Impact factor: 49.962

2.  Covid-19 and Health Care's Digital Revolution.

Authors:  Sirina Keesara; Andrea Jonas; Kevin Schulman
Journal:  N Engl J Med       Date:  2020-04-02       Impact factor: 91.245

3.  Virtually Perfect? Telemedicine for Covid-19.

Authors:  Judd E Hollander; Brendan G Carr
Journal:  N Engl J Med       Date:  2020-03-11       Impact factor: 91.245

Review 4.  Big data and black-box medical algorithms.

Authors:  W Nicholson Price
Journal:  Sci Transl Med       Date:  2018-12-12       Impact factor: 17.956

5.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

6.  Associative processes in intuitive judgment.

Authors:  Carey K Morewedge; Daniel Kahneman
Journal:  Trends Cogn Sci       Date:  2010-08-07       Impact factor: 20.229

7.  Telehealth transformation: COVID-19 and the rise of virtual care.

Authors:  Jedrek Wosik; Marat Fudim; Blake Cameron; Ziad F Gellad; Alex Cho; Donna Phinney; Simon Curtis; Matthew Roman; Eric G Poon; Jeffrey Ferranti; Jason N Katz; James Tcheng
Journal:  J Am Med Inform Assoc       Date:  2020-06-01       Impact factor: 4.497

Review 8.  The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries.

Authors:  Jonathan Guo; Bin Li
Journal:  Health Equity       Date:  2018-08-01

9.  Conversational agents in healthcare: a systematic review.

Authors:  Liliana Laranjo; Adam G Dunn; Huong Ly Tong; Ahmet Baki Kocaballi; Jessica Chen; Rabia Bashir; Didi Surian; Blanca Gallego; Farah Magrabi; Annie Y S Lau; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2018-09-01       Impact factor: 4.497

10.  Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage.

Authors:  Tadahiro Goto; Carlos A Camargo; Mohammad Kamal Faridi; Robert J Freishtat; Kohei Hasegawa
Journal:  JAMA Netw Open       Date:  2019-01-04
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  4 in total

1.  Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic.

Authors:  Darius-Aurel Frank; Christian T Elbæk; Caroline Kjær Børsting; Panagiotis Mitkidis; Tobias Otterbring; Sylvie Borau
Journal:  PLoS One       Date:  2021-11-22       Impact factor: 3.240

2.  Artificial intelligence in peritoneal dialysis: general overview.

Authors:  Qiong Bai; Wen Tang
Journal:  Ren Fail       Date:  2022-12       Impact factor: 3.222

3.  A single latent channel is sufficient for biomedical glottis segmentation.

Authors:  Andreas M Kist; Katharina Breininger; Marion Dörrich; Stephan Dürr; Anne Schützenberger; Marion Semmler
Journal:  Sci Rep       Date:  2022-08-22       Impact factor: 4.996

4.  Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence.

Authors:  Anne-Marie Nussberger; Lan Luo; L Elisa Celis; M J Crockett
Journal:  Nat Commun       Date:  2022-10-03       Impact factor: 17.694

  4 in total

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