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Identifying Ethical Considerations for Machine Learning Healthcare Applications

Char, Danton S. ; Abràmoff, Michael D. ; Feudtner, Chris

American journal of bioethics, 2020-11, Vol.20 (11), p.7-17 [Periódico revisado por pares]

United States: Taylor & Francis

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  • Título:
    Identifying Ethical Considerations for Machine Learning Healthcare Applications
  • Autor: Char, Danton S. ; Abràmoff, Michael D. ; Feudtner, Chris
  • Assuntos: Artificial intelligence ; Bioethics ; Delivery of Health Care ; effectiveness ; ethics ; Humans ; Machine Learning ; Morals ; safety ; test characteristics
  • É parte de: American journal of bioethics, 2020-11, Vol.20 (11), p.7-17
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Along with potential benefits to healthcare delivery, machine learning healthcare applications (ML-HCAs) raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure the overall problem of evaluating these technologies, especially for a diverse group of stakeholders. This paper outlines a systematic approach to identifying ML-HCA ethical concerns, starting with a conceptual model of the pipeline of the conception, development, implementation of ML-HCAs, and the parallel pipeline of evaluation and oversight tasks at each stage. Over this model, we layer key questions that raise value-based issues, along with ethical considerations identified in large part by a literature review, but also identifying some ethical considerations that have yet to receive attention. This pipeline model framework will be useful for systematic ethical appraisals of ML-HCA from development through implementation, and for interdisciplinary collaboration of diverse stakeholders that will be required to understand and subsequently manage the ethical implications of ML-HCAs.
  • Editor: United States: Taylor & Francis
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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