I was making a general point, perhaps I am being naive but in which case can you explain what relevance a post code would have for MRI imaging data used to train say a deep learning algo for a diagnostic tool? Why include it?
Abstract data is very rarely useful without demographic details. Let's say your diagnostic tool was looking at lesions prevalence, location and change for a diagnostic tool for the management of MS. Interesting and useful info that could also shape the picture at that time would be bloodwork, age group, gender, ethnicity etc using a postcode would give you some useful data as it would hint towards a socio-economic group. Now I could scan that image in no time and instantly see areas of whitening (eg the lesions) and compare and contrast with a previous version - that's a doddle - what I can't do though is trawl though all the other factors and compute how they may then be related. From that we can get patterns - now we've moved beyond some simple diagnostic tool which is fairness is doing us no favours and not improving efficiency or efficacy and into the realms of epidemiology where we can look at where and why the disease pattern may be occurring.
Now there are times when raw result analysis would be good eg Demis Hassabis's lot looking at the creatinine and urea levels as early indicators of renal failure worsening etc but even then without extra information you're really wasting your time. Now I don't know whether you are medical or not but you tell me which would tell you the most.
Case 1
Urea and Creatinine levels increasing steadily over time. (these being indicators for effective renal functioning)
Case 2
Urea and Creatinine levels increasing steadily over time in a teenager boy from a lower socio-economic postcode who has previously had a kidney transplant.
Now Case 1 would maybe tell you there is an issue but you that's pretty obvious stuff is getting worse. Case 2 may though get flagged up as an important aberration in an age group that is known to have compliance issues with taking medicine post transplant. That gives you a solution.
So in answer to your question it's quite simple - how can the tool learn without context. The first thing your doctor will ask at a hospital is history - that gives you context. Doctor my leg hurts is rather banal - doctor my legs hurt cause I am a muppet who jumped off a 3 m wall would give them a good clue where to look! Otherwise we are getting a machine to do a job it shouldn't and needn't and working to its actual strengths.
Edit: If you are just talking about say classification eg tumour grading etc then that is really a narrow subset of the actual passed on data.