Revolutionizing Musculoskeletal Radiology: Artificial Intelligence–assisted Magnetic Resonance Imaging Protocol Generation for Sports Injuries

Purpose or Learning Objective: Optimizing magnetic resonance imaging protocoling for sports injuries is crucial for image quality and accurate diagnoses. Manually navigating guidelines for protocol selection is time consuming and prone to error. Large language models offer potential automation but may lack training on region-specific magnetic resonance imaging guidelines. Retrieval-augmented generation–enabled models could address this limitation by integrating external knowledge into large language model prompts. This study evaluates the performance of a baseline and retrieval-augmented generation–enabled large language model in generating magnetic resonance imaging protocols for musculoskeletal sports injuries.

Methods or Background: The European Society of Musculoskeletal Radiology 2016 Guidelines for MR Imaging of Sports Injuries were used to develop a custom architecture that retrieves protocol parameters for 19 body regions of the musculoskeletal system. GPT-4o was prompted with and without retrieval-augmented generation to produce guideline-based recommendations for each magnetic resonance imaging sequence. The evaluated parameters included field of view (max), slice thickness (max), echo time, and matrix size (min). Additionally, both models provided patient positioning recommendations. Completeness and adherence to European Society of Musculoskeletal Radiology guidelines were assessed, with adherence rates compared between the retrieval-augmented generation and non–retrieval-augmented generation models. Subgroup analyses were conducted by magnetic resonance imaging parameter and body region.

Results or Findings: A total of 109 magnetic resonance imaging sequences were identified from the European Society of Musculoskeletal Radiology guidelines, yielding 436 parameters for evaluation. Both models achieved 100% completeness in parameter output generation. Adherence rates were 97.5% for the retrieval-augmented generation model versus 31.9% for the non–retrieval-augmented generation model. McNemar's test (χ² = 278.2; P < 0.001) indicated a significant difference between models. Subgroup analyses showed significant improvements across all parameters (P < 0.05), with the largest increases in slice thickness (98.2% versus 25.5%) and matrix size (100% versus 32.1%).

Adherence also significantly improved across all body regions, with the greatest improvement observed for spine sequences (80% versus 0%), and the smallest for the shoulder (77.5% versus 54.2%). For patient positioning recommendations, the retrieval-augmented generation model achieved 94.7% adherence versus 68.4% for the non–retrieval-augmented generation model (P < 0.05).

Conclusion: This is the first study to develop a custom retrieval-augmented generation–enabled system for magnetic resonance imaging protocoling in musculoskeletal radiology. Retrieval-augmented generation–based prompts significantly improved adherence to magnetic resonance imaging guidelines across parameters, body regions, and positioning recommendations, demonstrating their potential for workflow optimization. Future work should focus on prospective clinical validation and integration with radiology communication systems.

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Artikel online veröffentlicht:
02. Juni 2025