Balancing Automation and Human Oversight in Healthcare AI

Abstract

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.

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