Natural Language Processing in Radiology: Update on Clinical Applications.

Area of Science:

• Medical Informatics
• Radiology
• Natural Language Processing

Background:

• Radiological reports contain critical clinical information but are often unstructured.
• Manual extraction of knowledge from these reports is time-consuming and expensive.
• Natural Language Processing (NLP) offers automated solutions for text analysis.

Approach:

• This study provides a comprehensive review of commonly used NLP methods for information extraction from radiological reports.
• It examines the development of tools designed to enhance radiological workflows using NLP-derived insights.
• The review also discusses current limitations and future trends in applying NLP to radiology.

Key Points:

• NLP techniques can significantly streamline the analysis of radiological reports.
• Automated extraction of clinical knowledge supports decision-making for healthcare professionals.
• NLP tools can improve the organization and accessibility of patient medical data.

Conclusions:

• NLP presents significant opportunities to enhance radiology practice and research.
• Further development and application of NLP tools are crucial for future advancements.
• Addressing limitations will be key to realizing the full potential of NLP in radiology.