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* **Model Validation:** *Practitioners must ensure their AI system functions as intended and is working correctly.* Validating AI models involves a rigorous process of testing, evaluation, and assessment to verify that the model's performance meets expectations and complies with ethical guidelines. This includes checking for bias, ensuring accuracy, and evaluating how the model performs under various conditions. The aim is to identify and address any potential weaknesses or vulnerabilities in the model, as well as to ensure that it functions as expected. Model validation involves a continuous process of monitoring, improvement, and feedback to ensure that the AI system remains reliable and trustworthy. A well-validated model helps practitioners build AI systems that are accurate, reliable, and trustworthy. For example, if an AI system is designed to predict the likelihood of a disease, it needs to be rigorously tested against real-world data to ensure its predictions are accurate. This testing should involve analyzing its performance on different patient populations and under various clinical scenarios.
