For better or worse, most of these articles are US-centric because that's where most of the R&D money for health ML is. The far more reasonable approach is to

For better or worse, most of these articles are US-centric because that's where most of the R&D money for health ML is. The far more reasonable approach is to make sure your data contains as many demographics as possible (not just race) from your actual patient population. If there happen to be gaps, then put in at least a reasonable effort to fill them instead of shrugging and saying "it's out of our control". That, along with a per-demographic breakdown of important metrics and your point about qualifying demographics in the data (which is already done in most medical publications, including many using ML) would already be a huge improvement on what most people do now. Ironically, it's the big tech companies that have the hardest time with this because they want to make generally deployable projects, yet don't have access to as much data as many healthcare orgs do. Frankly, I don't have much sympathy for them: a lot of this is trust issues from self-inflicted damage.