How Can Artificial Intelligence Help Avoid Mistakes in Musculoskeletal Imaging?

Abstract

Musculoskeletal imaging plays a central role in diagnosing and managing a wide range of orthopedic conditions. However, it remains susceptible to both interpretive and noninterpretive errors, amplified by increasing imaging demand and complexity. Artificial intelligence, especially deep learning and large language models, has shown growing potential to reduce these errors at every stage of the imaging workflow. From optimizing exam requests and imaging protocols to reducing artifacts and improving interpretative consistency, artificial intelligence supports radiologists in enhancing diagnostic accuracy, efficiency, and reproducibility. Applications now extend across all modalities, including magnetic resonance, radiography, computed tomography, and ultrasound, and they address common pitfalls such as subjective assessments and measurement variability. Post-interpretation tools using large language models further improve report clarity and patient communication. Although integration into clinical practice remains ongoing, artificial intelligence already offers a transformative opportunity to improve musculoskeletal imaging quality and safety through collaborative human-machine interaction.