Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

X-ray Imaging

X-ray Imaging

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...

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

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...

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

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...

You might also read

Related Articles

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

Sort by

Same author

A Human Pharmacogenomics Approach Provides Insight Into the Pathogenesis and Pathophysiology of Steroid-Induced Ocular Hypertension.

Ophthalmology science·2026

Same author

Patterning human kidney organoids with synthetic Wnt-secreting organizers.

Science (New York, N.Y.)·2026

Same author

How does maternal "belief in a dangerous world" affect maternal and child depressive symptoms during times of crisis?

Journal of affective disorders reports·2026

Same author

A pilot study of brain correlates of long-term treatment with transcutaneous vagal nerve stimulation in posttraumatic stress disorder.

Journal of affective disorders reports·2026

Same author

Abdominal fat distribution phenotypes and metabolic risk in premenopausal Asian women: insights beyond general adiposity measures.

Scientific reports·2026

Same author

Making Sense of Mollicute Screening in Lung Transplant Recipients: Challenges and Opportunities.

Transplant infectious disease : an official journal of the Transplantation Society·2026

Search research articles

Related Experiment Video

Updated: Sep 19, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Efficiency and Quality of Generative AI-Assisted Radiograph Reporting.

Jonathan Huang^1,2, Matthew T Wittbrodt³, Caitlin N Teague³

¹Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

JAMA Network Open

|June 5, 2025

Summary

Generative artificial intelligence (AI) in radiology improves radiologist efficiency by 15.5% without compromising report quality. The AI model also shows promise in detecting critical pneumothorax cases.

More Related Videos

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

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

Published on: July 11, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

Related Experiment Videos

Last Updated: Sep 19, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

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

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

Published on: July 11, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

Area of Science:

Radiology
Artificial Intelligence
Medical Informatics

Background:

Diagnostic imaging interpretation requires synthesizing complex clinical data into reports.
Generative artificial intelligence (AI) offers potential for augmenting this process.
The clinical impact of AI-drafted radiological reports remains largely unstudied.

Purpose of the Study:

To prospectively evaluate the impact of a workflow-integrated generative AI model on radiologist documentation efficiency.
To assess the clinical accuracy and textual quality of AI-assisted final radiology reports.
To determine the AI model's capability in detecting clinically significant pneumothorax.

Main Methods:

A prospective cohort study was conducted at a tertiary academic health system.
Radiologist documentation efficiency was compared between AI-assisted and non-assisted reports.
Clinical accuracy and textual quality were assessed via peer review.
The AI model's performance in flagging pneumothorax was evaluated for sensitivity and specificity.

Main Results:

AI-assisted interpretations were 15.5% faster (159.8 vs 189.2 seconds; P=.02).
No significant differences were found in clinical accuracy (P=.41) or textual quality (P=.06).
The AI model achieved 72.7% sensitivity and 99.9% specificity for detecting pneumothorax requiring intervention.

Conclusions:

Generative AI integration in radiology enhances documentation efficiency while maintaining report quality.
The AI model demonstrated effectiveness in identifying critical pneumothorax cases.
Radiologist-AI collaboration shows potential to improve clinical care delivery.