Exploring the Ethical Challenges in the Design and Auditing of a Machine Learning Computer Vision Algorithm for Clinical Use

Abstract

Faulty prediction models and hidden racial biases have been found in algorithms affecting the care of hundreds of millions of patients, bringing the apparent risks of the integration of artificial intelligence into healthcare to public attention. This retrospective case study describes the most ethically salient issues encountered by those involved in the design of a deep learning-powered computer vision radiology algorithm intended for use in clinical care. The participants of this study perceived that the cultures and expectations associated with professional academic norms in AI and adjacent disciplines tended to promote the goals of AI innovation over the needs of health systems, or specific clinical use cases. They identified several ways in which more prescriptive regulatory requirements could better meet the needs of health systems. When participants situated the design process within its social, economic, or historical contexts, they took a more reflexive approach to the ethical issues they encountered.

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