What does sensitivity (recall) measure in an AI system?

Sensitivity, also known as recall, is a crucial metric in evaluating the performance of an AI system, particularly in the context of classification problems where distinguishing between positive and negative cases is essential. Sensitivity specifically measures the system's ability to correctly identify positive cases out of all actual positive cases. In other words, it calculates the proportion of true positive results compared to the total number of positive instances, which indicates how effectively the AI can detect instances of the condition it is designed to identify.

For example, in a dental AI system aimed at detecting cavities, sensitivity will quantify how many of the actual cavities (positive cases) were correctly identified by the AI. A high sensitivity means that the model is very good at detecting positive cases, which is critical in preventing undiagnosed conditions and providing timely treatment.

This focus on positive cases sets sensitivity apart from other metrics, such as specificity, which looks at true negatives, or overall accuracy, which considers both true positives and true negatives. Therefore, understanding sensitivity is essential for clinicians to assess how well an AI tool can identify relevant health conditions, ensuring better patient outcomes.