For authors Effy Vayena, Alessandro Blasimme, and I. Glenn Cohen, the ethical challenges relating to machine learning (ML) in medicine can manifest in three stages: data sourcing, product development, and clinical deployment.
In order for any ML-based health device/algorithm to fully perform its purpose with minimal unintended consequences, the developer of such technology must adhere to the following conditions: (1) the data used for training the ML device/algorithm followed data protection and privacy requirements, (2) the commitment to fairness was organically built-in to the development process, and (3) there is transparency in the efficacy results of the ML device/algorithm once it has been deployed [read this article to know What Does An Ethical AI Look Like In Health Care]
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