You aren't as ill as they say you are.

From Accuracy of Practitioner Estimates of Probability of Diagnosis Before and After Testing by Daniel J. Morgan, Lisa Pineles, Jill Owczarzak, et al.  The Abstract is a little abstruse.  The meat is in the conclusion:

This survey study suggests that for common diseases and tests, practitioners overestimate the probability of disease before and after testing. Pretest probability was overestimated in all scenarios, whereas adjustment in probability after a positive or negative result varied by test. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

The effect sizes were not small with over-diagnosis by several factors.  

The issue appears to less about medical knowledge and more about how to interpret statistical tests.

Scenarios requesting identical test interpretation based on hypothetical numbers revealed similar tendencies. For the question, “A test to detect a disease for which prevalence is 1 out of 1000 has a sensitivity of 100% and specificity of 95%. What is the chance that a person found to have a positive result actually has the disease?” the median answer was 95% (IQR, 95%-100%), whereas the correct answer was 2%. For the related question, “What is the chance that a person found to have a negative result actually has the disease?” the median answer was 5% (IQR, 0%-5%), whereas the correct answer was 0%.