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

Computed Tomography01:10

Computed Tomography

6.8K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
6.8K
Positron Emission Tomography01:29

Positron Emission Tomography

6.0K
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...
6.0K
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

185
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...
185
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

78
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
78

You might also read

Related Articles

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

Sort by
Same author

Multiparametric MRI Model Predicts Parenchymal Hematoma in Acute Ischemic Stroke After Reperfusion.

AJNR. American journal of neuroradiology·2026
Same author

Are magnetic resonance imaging features associated with intermittent and constant pain in knee osteoarthritis? A cross-sectional study.

Osteoarthritis and cartilage open·2026
Same author

Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.

NPJ digital medicine·2026
Same author

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation.

Journal of biomedical informatics·2026
Same author

Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Regional and depth-dependent associations between subchondral bone and cartilage in hip osteoarthritis: a preliminary [<sup>18</sup>F]-NaF PET-MR study exploring bone-cartilage cross-talk.

Skeletal radiology·2026

Related Experiment Video

Updated: Oct 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

706

Clinical language search algorithm from free-text: facilitating appropriate imaging.

Gunvant R Chaudhari1, Yeshwant R Chillakuru1,2, Timothy L Chen1,3

  • 1Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.

BMC Medical Imaging
|February 5, 2022
PubMed
Summary
This summary is machine-generated.

A new natural language processing (NLP) algorithm enhances access to the American College of Radiology (ACR) Appropriateness Criteria (AC). This tool automatically matches physician imaging orders to relevant AC guidelines, improving clinical decision support.

Keywords:
Appropriateness criteriaInformation retrievalNatural language processingTerm frequency-inverse document frequency

More Related Videos

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

7.0K
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

16.1K

Related Experiment Videos

Last Updated: Oct 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

706
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

7.0K
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

16.1K

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Decision Support

Background:

  • The American College of Radiology (ACR) Appropriateness Criteria (AC) is a valuable resource for evidence-based imaging decision support.
  • However, its underutilization by clinicians limits its impact on patient care.
  • There is a need for improved methods to integrate AC guidelines into clinical workflows.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) search algorithm.
  • The algorithm aims to automatically match clinical indications from physician imaging orders to appropriate ACR Appropriateness Criteria (AC) recommendations.
  • Facilitate the utilization of imaging recommendations for better clinical decision-making.

Main Methods:

  • A hybrid NLP model combining semantic similarity (sent2vec) and term frequency-inverse document frequency (TF-IDF) features was employed.
  • AC documents were ranked using cosine distance between query and document embeddings.
  • The algorithm was tested on simulated and real-world clinical indications from radiology reports.

Main Results:

  • The algorithm achieved high accuracy, ranking correct AC guidelines within the top 3 for 98% of simple and 85% of complex simulated indications.
  • On real-world data, 86% of indications with a single match were ranked in the top 3.
  • The NLP algorithm outperformed a custom Google search engine, particularly for complex queries.

Conclusions:

  • An effective NLP algorithm has been developed to match clinical indications with ACR Appropriateness Criteria (AC) guidelines.
  • This technology can be integrated into imaging ordering systems for seamless, automated guideline access.
  • The developed algorithm has the potential to enhance the use of evidence-based imaging practices.