To evaluate the technical challenges, methods, and clinical opportunities surrounding the interpretability and transparency of artificial intelligence models in radiology.
Narrative review synthesizing methodological frameworks for explainable artificial intelligence (XAI) in radiological image analysis.
Opaque 'black-box' deep learning models present significant barriers to trust, accountability, and clinical adoption in diagnostic imaging.
Developing robust interpretability frameworks offers critical opportunities to validate algorithmic reasoning, detect biases, and safely integrate artificial intelligence into radiological practice.