Evolution of Large Language Models in Radiology Structured Reporting

Overview

Structured reporting (SR) in radiology has evolved from early standardization efforts to the integration of advanced large language models (LLMs). LLMs, based on transformer architectures, offer promising solutions to improve SR adoption by enhancing report quality, reducing workflow disruptions, and enabling nuanced language understanding.

Background

Structured reporting aims to standardize radiology reports to improve quality, data accessibility, and guideline compliance. Early efforts included the development of standardized nomenclature and templates, such as RadLex® and RadReport, supported by professional societies like the ACR and RSNA. Despite benefits, SR adoption has been limited by workflow challenges and template rigidity. Advances in natural language processing (NLP), especially the emergence of transformer-based LLMs, have introduced new opportunities to integrate SR more seamlessly into clinical practice.

Data Highlights

Key milestones in SR and NLP evolution include:
- 1922: Early call for standardized X-ray report nomenclature
- 1991: ACR's first communication guideline
- 2006: Introduction of RadLex® lexicon
- 2018: ESR white paper advocating international SR collaboration
- 2018: Introduction of transformer models like BERT
- Recent: Adoption of LLMs with billions of parameters for complex NLP tasks

Key Findings

• Structured reporting improves report quality, reduces errors, and enhances guideline adherence but can be rigid and time-consuming.
• Early NLP models faced challenges with data sparsity and limited contextual understanding in medical language.
• LSTM networks improved long-range text pattern recognition but were computationally intensive for long texts.
• Transformer-based models like BERT enabled deeper contextual understanding and efficient large-scale training.
• Large language models (LLMs) with billions of parameters can follow instructions and handle complex language tasks, offering potential to integrate SR into radiologists’ workflows.
• Visual language models extend transformer architectures to process images alongside text, relevant for radiology applications.

Clinical Implications

The integration of LLMs into radiology structured reporting may reduce the manual burden of template selection and data entry, improving workflow efficiency. Enhanced language understanding by LLMs can help radiologists convey nuanced interpretations within structured formats, potentially increasing adoption and report quality. Clinicians should remain aware of ongoing developments and regulatory considerations as these technologies evolve.

Conclusion

The evolution from early standardization efforts to advanced LLMs marks significant progress in radiology structured reporting. Leveraging LLMs holds promise to overcome previous limitations and facilitate broader clinical implementation of SR.

References

1. Hickey et al 1922 -- Early call for standardized X-ray report nomenclature
2. Nobel et al -- Definition of structured reporting
3. American College of Radiology 1991 -- Guideline for Communication: Diagnostic Radiology
4. Radiological Society of North America 2006 -- Introduction of RadLex®
5. European Society of Radiology 2018 and update -- White paper on structured reporting
6. Vaswani et al 2018 -- Introduction of transformer models like BERT
7. Recent literature -- Definition and applications of large language models