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This study investigated the performance, explainability, and robustness ofdeployed artificial intelligence (AI) models in predicting mortality during theCOVID-19 pandemic and beyond. The first study of its kind, we found thatBayesian Neural Networks (BNNs) and intelligent training techniques allowed ourmodels to maintain performance amidst significant data shifts. Our resultsemphasize the importance of developing robust AI models capable of matching orsurpassing clinician predictions, even under challenging conditions. Ourexploration of model explainability revealed that stochastic models generatemore diverse and personalized explanations thereby highlighting the need for AImodels that provide detailed and individualized insights in real-world clinicalsettings. Furthermore, we underscored the importance of quantifying uncertaintyin AI models which enables clinicians to make better-informed decisions basedon reliable predictions. Our study advocates for prioritizing implementationscience in AI research for healthcare and ensuring that AI solutions arepractical, beneficial, and sustainable in real-world clinical environments. Byaddressing unique challenges and complexities in healthcare settings,researchers can develop AI models that effectively improve clinical practiceand patient outcomes.

Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond