In the context of AI and healthcare, what does 'external validation' refer to?

In the context of AI and healthcare, 'external validation' specifically refers to the process of validating AI algorithms against independent datasets that were not used during the training phase. This is a crucial step in ensuring that the AI model can generalize well to new, unseen data and is not overly fitted to the specifics of the training data. By assessing the model's performance on an independent dataset, healthcare practitioners and researchers can gauge its effectiveness in real-world scenarios. This process helps to establish the reliability and accuracy of the AI system, making it a vital component for adoption in clinical settings.

The other options do not accurately capture the essence of external validation. Using in-house data for training pertains to the development of the AI model, while gathering feedback only from internal staff lacks the objective assessment that independent datasets provide. Conducting promotional evaluations typically focuses on marketing or showcasing a product rather than on the scientific validation of AI performance.