Real-world usage diminishes validity of Artificial Intelligence tools

Abstract

Abstract Background Substantial effort has been directed towards demonstrating use cases of Artificial Intelligence in healthcare, yet limited evidence exists about the long-term viability and consequences of machine learning model deployment. Methods We use data from 130,000 patients spread across two large hospital systems to create a simulation framework for emulating real-world deployment of machine learning models. We consider interactions resulting from models being re-trained to improve performance or correct degradation, model deployment with respect to future model development, and simultaneous deployment of multiple models. We simulate possible combinations of deployment conditions, degree of physician adherence to model predictions, and the effectiveness of these predictions. Results Model performance shows a severe decline following re-training even when overall model use and effectiveness is relatively low. Further, the deployment of any model erodes the validity of labels for outcomes linked on a pathophysiological basis, thereby resulting in loss of performance for future models. In either case, mitigations applied to offset loss of performance are not fully corrective. Finally, the randomness inherent to a system with multiple deployed models increases exponentially with adherence to model predictions. Conclusions Our results indicate that model use precipitates interactions that damage the validity of deployed models, and of models developed in the future. Without mechanisms which track the implementation of model predictions, the true effect of model deployment on clinical care may be unmeasurable, and lead to patient data tainted by model use being permanently archived within the Electronic Healthcare Record.

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