What does data retraining aim to achieve in AI modeling?

Data retraining is an essential process in AI modeling that focuses on updating the model with new data to maintain performance over time. As the environment and underlying data distributions change, models can become less effective if they rely solely on the initial training data. By retraining with fresh, relevant data, the model adapts to these changes, improving its accuracy and reliability in making predictions or classifications.

This continual improvement is vital in fields such as dentistry, where new techniques, treatments, and patient data might evolve, impacting how AI systems function. Regular data retraining helps ensure that the AI's decisions and recommendations reflect the most current information and practices, thus enhancing its effectiveness in real-world applications.