Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

116
Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
116
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

290
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
290
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

154
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
154
Positron Emission Tomography01:29

Positron Emission Tomography

4.3K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
4.3K
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.2K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.2K
X-ray Imaging01:24

X-ray Imaging

5.6K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
5.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of structured training in osteotomy and splitting for BSSO using a 3D-printed mandibular model.

BMC medical education·2026
Same author

Large language models enable prognostic stratification of cancer patients using real-world clinical notes.

PLOS digital health·2026
Same author

Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.

Journal of medical Internet research·2026
Same author

Challenges in AI Based Tumor Board Case Summarization and Recommendations.

Research square·2026
Same author

Recent Advances in Automated Mitosis Detection in Digital Pathology: A PRISMA-Guided Systematic Review with Evaluation-Regime Stratification (2018-2025).

Biomedicines·2026
Same author

Can one model fit all? Evaluating foundation models for time series forecasting across clinical medicine.

Artificial intelligence in medicine·2026

Related Experiment Video

Updated: Jul 21, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K

Information extraction from weakly structured radiological reports with natural language queries.

Amin Dada1, Tim Leon Ufer2, Moon Kim2

  • 1Institute of AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany. amin.dada@uk-essen.de.

European Radiology
|July 28, 2023
PubMed
Summary

We developed a machine learning model using BERT to efficiently extract information from radiology reports. This natural language processing approach improves accuracy and saves radiologists time, ultimately benefiting patient care.

Keywords:
Information extractionMachine learningNatural language processing

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Jul 21, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Medical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Radiology reports contain crucial patient information but are often weakly structured.
  • Extracting specific data from these reports manually is time-consuming and inefficient for physicians and researchers.

Purpose of the Study:

  • To develop and evaluate natural language processing (NLP) machine learning models for efficient information extraction from radiology reports.
  • To provide physicians and researchers with an automated tool for querying radiology report data.

Main Methods:

  • Evaluated seven German BERT models on 857,783 unlabeled radiology reports.
  • Utilized an annotated reading comprehension dataset (SQuAD 2.0 format) of 1223 reports for fine-tuning and evaluation.
  • Compared models based on performance metrics including F1-score and exact match accuracy.

Main Results:

  • Continued pre-training of a BERT model on radiology data and a medical encyclopedia yielded the best performance.
  • The top model achieved an F1-score of 83.97% for answerable questions and 96.01% accuracy for unanswerable questions.
  • The model demonstrated stability against variations in question phrasing and handling of out-of-domain questions.

Conclusions:

  • BERT models pre-trained on radiological data significantly enhance question-answering accuracy for radiology reports.
  • The developed NLP approach offers an efficient method for information extraction, reducing manual workload for radiologists.
  • Integrating this tool into clinical workflows can free up radiologist resources and indirectly improve patient care.