Balancing Automation and Human Oversight in Healthcare AI

AI has influenced the healthcare domain significantly, improving diagnostic precision and predictive modeling. However, to ensure safety, trust, and reliability in AI-driven decision-making, there needs to be a balance between automation and human oversight. This review will discuss how simulated human feedback can be utilized to improve model performance by identifying and correcting low-confidence predictions. We train baseline models, Logistic Regression and ResNet-50, on MIMIC-IV and CheXpert datasets, respectively, while introducing mechanisms of oversight for refining the predictions. This reflects in various evaluation metrics used here, like AUC-ROC, precision, and recall. Our results underline the necessity of adaptive control mechanisms in AI for healthcare applications.