That all sounds shitty but I don't see how that's valuable information. They did eventually solve the problem and there's no comparison to some ML success story.
Human minds can be really good at diagnostics and still fail sometimes when faced with very difficult cases.
In my experience, ML would just classify everything as a very common disease and people would call it a success because it has an 80% effectiveness rate.
The problem that needs to be solved is a case like your example, not diagnosing the common cold.
One of the points taken should be that either via ML or human diagnostic is that these rare problems are either not diagnosed for long periods of time reducing quality of life or diagnosed posthumously.
The reduction of these measures are what we should use when making meat vs machine efficiency correlations.
Human minds can be really good at diagnostics and still fail sometimes when faced with very difficult cases.
In my experience, ML would just classify everything as a very common disease and people would call it a success because it has an 80% effectiveness rate.
The problem that needs to be solved is a case like your example, not diagnosing the common cold.