Identifying Ethical Considerations for Machine Learning Healthcare Applications

By Danton S. Char, Michael David Abràmoff, Chris Feudtner. American Journal of Bioethics, 2020

315 citations recorded by the source corpus.

Abstract 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.

Subfields: Health Informatics, Public Health, Environmental and Occupational Health, Safety Research

View Paper: https://www.semanticscholar.org/paper/6e6c03c3a971ca0d17f21bd094beaca51b514676

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