What does the term 'Monitoring' refer to in the AI lifecycle?

In the AI lifecycle, 'Monitoring' primarily refers to the practice of ensuring the quality and reliability of AI systems. This involves continuously assessing the performance of AI algorithms and models in real-world applications to detect any deviations or anomalies that may arise over time. Monitoring is critical because AI systems can degrade in performance due to changes in data patterns, user behavior, or environmental factors, necessitating adjustments or retraining.

By focusing on maintaining the quality and reliability of AI outputs, this aspect of the lifecycle helps ensure that the AI continues to function effectively, meets the desired standards, and provides accurate and relevant results. It is an ongoing process that aims to mitigate risks associated with deploying AI technologies in various fields, including dentistry. This is essential to uphold the trust and confidence of practitioners and patients in AI-assisted applications.