[Transformation of free-text radiology reports into structured data]

Area of Science:

• Natural Language Processing in Medical Imaging
• Artificial Intelligence in Radiology
• Computational Linguistics for Healthcare

Context:

• Radiology reports contain critical patient information in unstructured text.
• Automated processing of these reports is essential for clinical decision support and research.
• Large Language Models (LLMs) offer advanced capabilities for medical text analysis.

Purpose:

• To explore the challenges and promising methods for transforming natural language radiology reports into structured data using LLMs.
• To analyze and compare various approaches, including rule-based systems, machine learning, and deep learning.
• To investigate strategies for ensuring the quality and reliability of extracted data.

Summary:

• LLMs demonstrate significant potential for structuring unstructured radiology reports.
• Deep learning models achieve high accuracy, particularly when trained on extensive datasets.
• Key challenges include handling linguistic ambiguities, abbreviations, and expression variability.

Impact:

• Structured data extraction enhances the utility of radiology reports for clinical decision support and research.
• Integrating LLMs with domain knowledge (e.g., ontologies) can improve system performance.
• Further research into contextual information integration and robust evaluation metrics is crucial for advancing the field.