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

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...

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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...

You might also read

Related Articles

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

Sort by

Same author

MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.

Research square·2026

Same author

A myco-management problem: improving utilization of fungal and mycobacterial smear and culture.

Journal of clinical microbiology·2026

Same author

Report on the 2025 DICOM WSI Connectathon.

Journal of pathology informatics·2026

Same author

Face aging rate quantifies change in biological age to predict cancer outcomes.

Nature communications·2026

Same author

In search of truth: evaluating concordance of AI-based anatomy segmentation models.

Journal of medical imaging (Bellingham, Wash.)·2026

Same author

Thymic health consequences in adults.

Nature·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

End-to-end reproducible AI pipelines in radiology using the cloud.

Dennis Bontempi^1,2,3, Leonard Nuernberg^1,2,3, Suraj Pai^1,2,3

¹Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.

Nature Communications

|August 13, 2024

Summary

Cloud infrastructure enables reproducible artificial intelligence (AI) in radiology. This approach ensures transparency from data retrieval to result analysis, accelerating clinical translation of AI tools.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 17, 2025

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

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

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

Area of Science:

Medical Imaging
Radiology
Artificial Intelligence

Background:

Artificial intelligence (AI) algorithms show promise for revolutionizing radiology.
Lack of transparency and reproducibility in published AI research hinders clinical translation.
Existing reporting guidelines offer limited practical solutions.

Purpose of the Study:

To demonstrate the potential of cloud-based infrastructure for transparent and reproducible AI radiology pipelines.
To address challenges in implementing and sharing AI research for clinical translation.

Main Methods:

Implemented end-to-end reproducible AI pipelines using cloud-hosted data and computing.
Demonstrated pipeline functionality for data pre-processing, deep learning inference, and post-processing.
Validated two AI-based cancer imaging biomarker use cases from recent literature.

Main Results:

Achieved end-to-end reproducibility in AI pipeline execution on the cloud.
Confirmed findings from existing literature and extended validation to new data.
Provided transparent and extensible AI pipeline examples for the oncology field.

Conclusions:

Cloud-based infrastructure facilitates the implementation and sharing of transparent, reproducible AI radiology pipelines.
This approach can accelerate the clinical translation of AI solutions in radiology and oncology.
Enhanced transparency and reproducibility are crucial for advancing AI in medical imaging.