1. Identifying Ethical Considerations for Machine Learning Healthcare Applications.
- Author
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Char, Danton S., Abràmoff, Michael D., and Feudtner, Chris
- Subjects
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ALGORITHMS , *ARTIFICIAL intelligence , *CONCEPTUAL structures , *MACHINE learning , *MEDICAL care , *MEDICAL informatics , *ARTIFICIAL neural networks , *PATIENT safety , *TECHNOLOGY - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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