ABSTRACT
Big Data, machine learning, and artificial intelligence are gaining importance in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress toward individually tailored preventive and therapeutic interventions. These applications belong to current research practice that is data-intensive, meaning that large amounts and varieties of data are being used. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. This chapter takes a critical perspective into understanding both hopes and hypes as well as risks and opportunities through this transformation. By focusing on sex and gender, the text discusses how performativity is a useful concept to unpack data and data practices. While two cases of research are discussed, the chapter moves beyond these cases to show how to learn from past research, enrich the repertoire for research, reduce sex and gender bias, and strengthen biobanks as infrastructures that do justice to differences.
