AI in Dentistry Practice Test 2025 – Your All-in-One Guide to Exam Success!

The quality and quantity of data directly affect model performance because models are fundamentally dependent on the data they are trained on. High-quality data captures the relevant features and variations in the real-world scenarios that the model will encounter, leading to better generalization and accuracy in predictions. Inadequate or poor-quality data, such as those with inaccuracies, biases, or noise, can lead to misleading results and can compromise the effectiveness of the model.

Furthermore, having a sufficient amount of good quality data ensures that the model is well-trained and can learn the underlying patterns. This is particularly crucial in dentistry, where variations in patient data such as demographics, health conditions, and treatment responses can significantly influence outcomes. Therefore, both the quality and the quantity of the data play a critical role in determining how well the model performs in practical applications.