  This paper proposes a statistical framework with which artificial
intelligence can improve human decision making. The performance of each human
decision maker is first benchmarked against machine predictions; we then
replace the decisions made by a subset of the decision makers with the
recommendation from the proposed artificial intelligence algorithm. Using a
large nationwide dataset of pregnancy outcomes and doctor diagnoses from
prepregnancy checkups of reproductive age couples, we experimented with both a
heuristic frequentist approach and a Bayesian posterior loss function approach
with an application to abnormal birth detection. We find that our algorithm on
a test dataset results in a higher overall true positive rate and a lower false
positive rate than the diagnoses made by doctors only. We also find that the
diagnoses of doctors from rural areas are more frequently replaceable,
suggesting that artificial intelligence assisted decision making tends to
improve precision more in less developed regions.
