What is a consequence of not monitoring deployed AI models?

Not monitoring deployed AI models can lead to increased risks associated with model failures. In the context of dentistry, for example, an AI model may be used for tasks such as diagnosing conditions from radiographic images or predicting patient outcomes. Without proper monitoring, the model may drift over time due to changes in data patterns, patient demographics, or treatment methodologies. This drift can result in decreased accuracy, leading to misdiagnoses or suboptimal treatment recommendations.

Moreover, unmonitored models may experience technical failures or biases that were not evident during initial training. These could introduce significant risks to patient safety and clinical efficacy. By failing to monitor the performance of these models continuously, dental practitioners could inadvertently adopt decisions that negatively impact patient care, generate liability issues, or incur financial costs due to ineffective outcomes.

The other options suggest improvements or benefits that would not logically arise from neglecting to monitor the models.