Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging

Abstract

Generative AI-including Generative Adversarial Networks, diffusion models, Large Language Models (LLMs), and more recently, vision-language models-is increasingly utilized in clinical practice for musculoskeletal imaging tasks such as disease diagnosis, image enhancement, image reconstruction, electronic health record summarization, and radiologic report generation. Integrating these technologies into radiology workflows can significantly advance radiology report generation, structured reporting, and patient-centered communication. However, challenges such as hallucination, bias, and performance drift remain persistent issues. Ensuring the safe and reliable use of LLMs in radiology requires domain-specific training, robust validation, and enhanced data privacy measures. This review summarizes available evidence regarding the potential utility of generative AI in musculoskeletal imaging and radiologic reporting, as well as the challenges and pitfalls in its application. Recommendations for future advancements and clinical translation are also discussed.