Identifying Ethical Considerations for Machine Learning Healthcare Applications. Issue 11 (1st November 2020)

• Record Type: Journal Article
• Authors: Char, Danton S.; Abràmoff, Michael D.; Feudtner, Chris
• Is Part Of: American journal of bioethics. Volume 20:Issue 11(2020)
• Page Start: 7
• Page End: 17
• Publication Date: 2020-11-01
• Subjects: Artificial intelligence -- effectiveness -- ethics -- machine learning -- safety -- test characteristics; Bioethics -- Periodicals; Medical ethics -- Periodicals; 174.957
• DOI: 10.1080/15265161.2020.1819469
• Languages: English
• ISSNs: 1526-5161

Abstract

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.