Identifying Ethical Considerations for Machine Learning Healthcare Applications

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.

Keywords:

• Artificial intelligence
• effectiveness
• ethics
• machine learning
• safety
• test characteristics

This article is referred to by:

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What Counts as “Clinical Data” in Machine Learning Healthcare Applications?

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An Evaluation of the Pipeline Framework for Ethical Considerations in Machine Learning Healthcare Applications: The Case of Prediction from Functional Neuroimaging Data

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Where Bioethics Meets Machine Ethics

Embedded Ethics Could Help Implement the Pipeline Model Framework for Machine Learning Healthcare Applications

Structural Disparities in Data Science: A Prolegomenon for the Future of Machine Learning

Addressing the “Wicked” Problems in Machine Learning Applications – Time for Bioethical Agility

Machine Learning in Healthcare: Exceptional Technologies Require Exceptional Ethics

It is Time for Bioethicists to Enter the Arena of Machine Learning Ethics

Machine Learning Healthcare Applications (ML-HCAs) Are No Stand-Alone Systems but Part of an Ecosystem – A Broader Ethical and Health Technology Assessment Approach is Needed

What’s in the Box?: Uncertain Accountability of Machine Learning Applications in Healthcare

A Framework to Evaluate Ethical Considerations with ML-HCA Applications—Valuable, Even Necessary, but Never Comprehensive

DISCLOSURE STATEMENT

Michael Abràmoff is founder and CEO of Digital Diagnostics, Inc., and has patents, patent applications, ownership, employment, and consultancy related to the subject of this article.

Additional information

Funding

This work was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number [grant number K01HG008498 to DC]. MDA was supported by P30 EY025580, and an unrestricted grant from Research to Prevent Blindness, New York, NY.