The Case for Greener Biomedical Data Science and How to Get There

Biomedical data science holds immense promise for improving human health. However, the field carries an environmental cost stemming from the energy- and resource-intensive nature of its computational infrastructure. This article provides a comprehensive review of the sector's nascent efforts to address its environmental harms from technical, ethical, sociological, and regulatory perspectives. We identify a growing consensus on the need for action and trace the slow progress of institutionalizing mitigation strategies. To accelerate this process, we propose learning from the historical institutionalization of animal research ethics, building on key lessons associated with legitimization and implementation. Finally, we outline persistent challenges associated with a rebound paradox, as well as the need to consider context-dependent issues of global equity and justice to ensure a responsible and sustainable future for the field.