That's not how this works. If the model does something like "this image is of race X, race X has more lung cancers, therefore this image is likely lung cancer", then it's not helpful to anyone. You need the model to evaluate based on the image itself, not based on correlations that it can infer from the image - fairness is just a happy byproduct of that.
I think what the parent comment meant was that you could force the model to divert its attention elsewhere if you removed race as a variable by making the training data uniform in terms of race. I think it's a smart thought, though I doubt it'd work due to the fuzziness of "race" as a construct. Even if you grouped people using some combination of their self-classified and/or observed racial identity, the model would probably start identifying (and thus start cheating using) even subtler "sub-racial" biomarkers.
If you ask me, it's probably more effective to compensate for the model's learned racial bias using weights derived from the model outputs via statistical analysis.
