As we use artificial intelligence more and more and depend
on machines for decision making, critical thinking becomes
more important. In particular topics like false positive results
and limits of detection. This was brought up earlier in a
misused when we apply statistics to test results. It indicates
that a given condition exists when in reality it does not.
This conclusion can many times result in unnecessary
testing and costs (both to clinical patients (anguish) and
The previous entry pointed out limits of detection series
by Deming. Two other notable contributions– one on being able
to compare quantities resulting from different methods (
My experience is where a physician recommends a
prescription for a chronic condition based on analyte
readings. The algorithm is designed to predict a per cent
likelihood of an outcome. However, the clinical testing
is much more complicated with false positives entering
the picture. Not all of the people have have a minimmum
value will reach that specific outcome. This is not
revealed in the analyte results.
This leads to needing to ask more questions of the
results and their “true meaning.” Often the physician
will not assess these critical elements leading to over-testing
and over prescribing.