Statistical Tests for Replacing Human Decision Makers with Algorithms
This work addresses the challenge of enhancing diagnostic accuracy in healthcare by integrating AI, though it appears incremental as it builds on existing statistical methods for decision replacement.
The paper tackles the problem of improving human decision-making in medical diagnostics by proposing a statistical framework to replace some human decisions with algorithmic recommendations, specifically in abnormal birth detection, achieving a higher true positive rate and lower false positive rate compared to doctors alone.
This paper proposes a statistical framework of using artificial intelligence to improve human decision making. The performance of each human decision maker is benchmarked against that of machine predictions. We replace the diagnoses made by a subset of the decision makers with the recommendation from the machine learning algorithm. We apply both a heuristic frequentist approach and a Bayesian posterior loss function approach to abnormal birth detection using a nationwide dataset of doctor diagnoses from prepregnancy checkups of reproductive age couples and pregnancy outcomes. 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.