To the Editor—The promise of machine learning (ML) to augment medical decision-making in dynamic care environments has yet to be fully realized because of a gap in how algorithms are translated to the bedside, sometimes known as the ‘AI chasm’1. The drivers of this gap are numerous and complex, but a central challenge relates to the integration of ML into complex decision-making processes and clinical workflows. The ML field has developed a more nuanced appreciation of the importance of having the “human in the loop” 2, but has yet to identify precisely how to optimize the human–ML interface to achieve maximal impact on key outcomes3. Cognitive load is a term used to reflect the mental effort required to perform a task, which can be immense in care environments where clinicians collate, integrate, filter, weigh and reason about patient data in real time. Cognitive overload is associated with medical errors and burnout and contributes to suboptimal care outcomes4, 5. ML is capable of decreasing the mental effort required to process immense amounts of biomedical data, yet its ability to do so is rarely, if ever, evaluated. We argue that cognitive load can and should be measured throughout the ML development cycle to maximize the potential for integration of ML into medicine and to improve patient and provider outcomes.