Fig. 1: Conceptual overview illustrating the contrast between traditional medical diagnosis and machine learning-based interpretation of knee acoustic emissions.

The left panels represent the conventional clinical workflow, where diagnostic reasoning is based on symptoms, imaging, and anatomical cues. The right panels show the proposed AI-based approach, where knee sounds are analyzed by a machine learning model, and the decision process is made transparent via explainable AI techniques. The illustrative analogy (e.g., highlighting a dog in an image) emphasizes how the model focuses on acoustically meaningful regions.