1 min readAddressing the ethical challenges of machine learning in medicine

If there is any field that will greatly benefit with AI and machine learning, it is medicine. But patients do not want to replace their human doctors with robots, nor do they want algorithms to use their personal data for experimentation and pattern recognition.

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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