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.
Keywords
musculoskeletal - artificial intelligence - large language model - convolutional neural
network - machine learning
