Machine Learning in Medical Systems

Abstract

Abstract Medicine and healthcare are crucial areas in the application of machine learning (ML) and artificial intelligence (AI). While ML promises to revolutionize healthcare, it also raises various social, ethical, and regulatory issues as well as novel sociological questions. This chapter sets a sociological agenda on ML in medical systems. After briefly explaining the ML applications in medicine and their practical concerns, it reviews how scholars in medical sociology, science and technology studies, critical data studies, and relevant fields have begun to study this topic. Five key themes are highlighted: imaginaries and expectations, politics of digital health data, algorithmic knowledge production, medical ML systems at work, and governance and ethics. All these areas have important practical implications and considerable potential for further research. Finally, the chapter draws upon the case of the Chinese medical AI industry to emphasize the importance of local contexts and nuances for the sociological agenda.