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

Data Validation

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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 II: MRI and Ventilation Perfusion Scan

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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...

You might also read

Related Articles

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

Sort by

Same author

Physics Insights into Liver MRI: Educational Guidance for Protocol Optimization.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026

Same author

Summary of Guidelines for Identifying and Risk-Stratifying Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease: A Primer for Family Physicians.

Diagnostics (Basel, Switzerland)·2026

Same author

Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of focal liver lesions.

Physics in medicine and biology·2026

Same author

Liver Nodule Anomaly Detection Using Ultra-sound Radiofrequency Signals and Variational Autoencoders.

IEEE transactions on bio-medical engineering·2026

Same author

Geographic and Sex Distribution of LI-RADS Research Globally: A Cross-Sectional Meta-Research Study.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026

Same author

Feasibility of Early Oral Nutritional Supplementation After Liver Transplantation: A Randomized Pilot Study.

Clinical transplantation·2026

Search research articles

Related Experiment Video

Updated: Aug 22, 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

Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

William Tanguay^1,2, Philippe Acar^1,2, Benjamin Fine^3,4

¹60352Centre hospitalier de l'Université de Montréal, Montréal, QC, Canada.

Canadian Association of Radiologists Journal = Journal L'Association Canadienne Des Radiologistes

|November 7, 2022

Summary

Artificial intelligence (AI) software in radiology needs standardized evaluation before clinical use. This framework provides tools for assessing AI systems to ensure patient safety and optimize resource allocation in the growing field of radiology AI.

Keywords:

artificial intelligence artificial intelligence software evaluation framework external validation health technology assessment

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

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

Related Experiment Videos

Last Updated: Aug 22, 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

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Artificial intelligence (AI) software is rapidly advancing in radiology, with continuous development and parallel validation of new applications.
Existing guidelines address reporting standards for AI research but lack recommendations for assessing AI software before adoption and after commercialization.
A formal system for AI assessment is crucial for patient safety, clinical workflow integration, and efficient resource management in the expanding radiology AI ecosystem.

Purpose of the Study:

To address the unmet need for AI software assessment recommendations in radiology.
To provide a framework for evaluating AI software before and after commercialization.
To streamline communication between the AI industry and healthcare providers.

Main Methods:

Development of a glossary for AI software types, use cases, and clinical roles.
Identification of healthcare needs, key performance indicators, and essential information for AI software assessment.
Presentation of example software performance metrics categorized by AI software type.
Proposal of a conceptual framework to aid decision-makers and radiologists in assessing AI software potential.

Main Results:

A structured approach to AI software assessment, including a glossary and performance metrics.
A framework designed to facilitate communication with AI vendors and support clinical decision-making.
The foundation for a radiologist-led prospective validation network for radiology AI software.

Conclusions:

Standardization of AI software specification, classification, and evaluation is essential due to the rapid growth of AI in radiology.
The Canadian Association of Radiologists' AI Tech & Apps Working Group advocates for an AI Specification document format.
Implementation of a clinical expert evaluation process for radiology AI software is supported.