Literature DB >> 31359302

The right to refuse diagnostics and treatment planning by artificial intelligence.

Thomas Ploug1, Søren Holm2,3.   

Abstract

In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to (1) the physician's role in the patients' formation of and acting on personal preferences and values, (2) the bias and opacity problem of AI systems, and (3) rational concerns about the future societal effects of introducing AI systems in the health care sector.

Entities:  

Keywords:  Artificial intelligence; Bias and discrimination; Health care; Rational concern; Right to refuse

Mesh：

Year:  2020        PMID： 31359302     DOI： 10.1007/s11019-019-09912-8

Source DB:  PubMed          Journal:  Med Health Care Philos        ISSN： 1386-7423


  14 in total

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Journal:  J Clin Epidemiol       Date:  2001-12       Impact factor: 6.437

Review 2.  Automation bias: a systematic review of frequency, effect mediators, and mitigators.

Authors:  Kate Goddard; Abdul Roudsari; Jeremy C Wyatt
Journal:  J Am Med Inform Assoc       Date:  2011-06-16       Impact factor: 4.497

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Review 4.  The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts.

Authors:  Brent Daniel Mittelstadt; Luciano Floridi
Journal:  Sci Eng Ethics       Date:  2015-05-23       Impact factor: 3.525

Review 5.  Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

Authors:  Steven E Dilsizian; Eliot L Siegel
Journal:  Curr Cardiol Rep       Date:  2014-01       Impact factor: 2.931

6.  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

7.  Automation bias: empirical results assessing influencing factors.

Authors:  Kate Goddard; Abdul Roudsari; Jeremy C Wyatt
Journal:  Int J Med Inform       Date:  2014-01-17       Impact factor: 4.046

Review 8.  Societal Issues Concerning the Application of Artificial Intelligence in Medicine.

Authors:  Alfredo Vellido
Journal:  Kidney Dis (Basel)       Date:  2018-09-03

9.  Google DeepMind and healthcare in an age of algorithms.

Authors:  Julia Powles; Hal Hodson
Journal:  Health Technol (Berl)       Date:  2017-03-16

10.  Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Authors:  Stephen F Weng; Jenna Reps; Joe Kai; Jonathan M Garibaldi; Nadeem Qureshi
Journal:  PLoS One       Date:  2017-04-04       Impact factor: 3.240
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  3 in total

1.  Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Authors:  Thomas P Quinn; Manisha Senadeera; Stephan Jacobs; Simon Coghlan; Vuong Le
Journal:  J Am Med Inform Assoc       Date:  2021-03-18       Impact factor: 4.497

2.  Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research.

Authors:  Milad Mirbabaie; Lennart Hofeditz; Nicholas R J Frick; Stefan Stieglitz
Journal:  AI Soc       Date:  2021-06-28

3.  Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Authors:  Frank Ursin; Cristian Timmermann; Marcin Orzechowski; Florian Steger
Journal:  Front Med (Lausanne)       Date:  2021-07-21
  3 in total

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