At the beginning of the COVID-19 pandemic, before the widespread adoption of reliable point-of-care assays to detect SARS-CoV-2, one highly active area of research involved the development of ML algorithms to estimate the probability of infection. These algorithms based their predictions on various data elements captured in electronic health records, such as chest radiographs. Despite their promising initial validation results, the success of numerous artificial neural networks trained on chest X-rays were largely not replicated when applied to different hospital settings, in part because the models failed to learn or understand the true underlying pathology of COVID-19. Instead, they exploited shortcuts or spurious associations that reflected biologically meaningless variations in image acquisition, such as laterality markers, patient positioning or differences in radiographic projection6. These ML algorithms were not explainable and, while appearing to be at the cutting edge, were inferior to traditional diagnostic techniques such as RT-PCR, obviating their usefulness.