Question 10
Domain 1: Fundamentals of AI and MLA healthcare company is building an AI solution to predict patient readmission within 30 days of patient discharge. The company has trained a model on historical patient data including medical history, demographics, and treatment specifications, to provide readmission predictions in real time. Which task describes AI model inference in this scenario?
Correct answer: D
Explanation
AI model inference is the stage where a trained model is applied to new data to produce a prediction. Here, the model is used "to predict patient readmission within 30 days" and "provide readmission predictions in real time," which describes inference rather than training.
Why each option is right or wrong
A. Gather historical patient readmission data.
Collecting historical data is a data preparation step, not generating predictions from a trained model.
B. Use appropriate metrics and assess model performance.
Assessing metrics evaluates model quality after training; it does not produce live patient predictions.
C. Use data to identify patient patterns and correlations.
Finding patterns and correlations describes training or analysis, not applying a trained model to new cases.
D. Use a trained model to predict patient readmission.
AI model inference is the deployment phase in which an already trained model is applied to incoming, unseen patient records to generate an output. In this scenario, the model has already been trained on historical data, and the live use case is to estimate whether a discharged patient will be readmitted within 30 days; that real-time prediction step is inference, not training.