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Related Experiment Video

Updated: Aug 6, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and

Tobias Jorg¹, Benedikt Kämpgen², Dennis Feiler³

¹Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany. tobias.jorg@unimedizin-mainz.de.

Insights Into Imaging

|March 17, 2023

Summary

This study introduces a novel tool that uses natural language processing (NLP) to convert dictated free text into structured radiology reports, enhancing efficiency and accuracy in clinical workflows.

Keywords:

Dialogue system Natural language processing Speech recognition Structured reporting

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Area of Science:

Radiology Informatics
Artificial Intelligence in Medicine
Natural Language Processing

Background:

Structured reporting (SR) offers advantages over free-text reporting (FTR) but faces adoption barriers.
Limited integration of speech recognition hinders SR efficiency in clinical radiology.
Current SR template completion is labor-intensive, limiting routine use.

Purpose of the Study:

To develop an NLP-driven tool integrating speech recognition with SR.
To leverage NLP to combine the benefits of SR and speech recognition for improved radiology reporting.

Main Methods:

Developed a reporting tool using NLP to convert dictated free text into structured reports.
Integrated a task-oriented dialogue system with visual feedback for radiologists.
Utilized NLP components and speech recognition to extract structured content for SR templates (RadLex terms).

Main Results:

The NLP tool achieved high accuracy in converting dictated text to structured reports.
F1 scores of 0.98 (fictitious samples) and 0.90 (original reports) demonstrated effective NLP recognition for urolithiasis CTs.
The tool successfully populated SR templates with extracted information.

Conclusions:

This novel tool integrates speech recognition into structured reporting, potentially transforming radiology reporting.
The system offers a significant advancement for clinical imaging workflows by improving efficiency and data standardization.