At what stage do you retrain the existing AI model or train a new model using updated data?

The correct choice, which refers to retraining the existing AI model, is pivotal in maintaining the performance and accuracy of the model. Model retraining occurs when new data becomes available that can provide additional insights or reflect changes in the environment or healthcare practices. This stage is crucial, as AI models can drift over time due to changes in the underlying data distribution, leading to decreased performance if not updated.

When retraining, the model is typically updated with new examples, which can improve its ability to make accurate predictions and provide better outcomes in applications such as medical billing and coding. This might involve adjusting the model's parameters based on the new data or possibly starting from scratch if the new data significantly changes the dynamics of the problem being solved.

In contrast, other stages like data verification, quality assurance, and feature engineering serve distinct purposes. Data verification ensures the integrity and accuracy of the data before it is used for training. Quality assurance involves checking the overall performance and functionality of the model but does not directly involve altering the model itself. Feature engineering focuses on selecting or transforming the input features used by the model but is separate from retraining the model based on updated data.