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

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

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...

Distribution Reliability and Automation

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

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

Sort by

Same author

Radiomic prediction of substantial LVSI in endometrial cancer using reduced field of view DWI- a feasibility study.

European journal of radiology·2026

Same author

Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology.

NPJ precision oncology·2026

Same author

Deep Learning Reconstruction in MRI: Unlocking Efficiency, Enhancing Image Quality, and Navigating Practical Limits.

AJNR. American journal of neuroradiology·2026

Same author

Sustained improvements in radiologist engagement and administrative alignment following structured leadership and wellness interventions: A single institution three-year observational study.

Clinical imaging·2026

Same author

AJNR Study-Specific Guidelines for AI in Medical Imaging: Bridging Gaps in Reference Standard and Clinical Evaluation.

AJNR. American journal of neuroradiology·2026

Same author

CT-based deep learning prediction of complete response in intermediate-stage hepatocellular carcinoma treated with drug-eluting beads transarterial chemoembolization.

BJR artificial intelligence·2026

Same journal

State-of-the-Art Epilepsy Imaging: Improving Tools for Epileptogenic Lesion Detection and Treatment.

Neuroimaging clinics of North America·2026

Same journal

Imaging of Epilepsy.

Neuroimaging clinics of North America·2026

Same journal

Implanted Devices for Management of Drug-Resistant Epilepsy: Background and MR Imaging Considerations.

Neuroimaging clinics of North America·2026

Same journal

Imaging of Epilepsy Surgery, Minimally Invasive Techniques, and Neuromodulation.

Neuroimaging clinics of North America·2026

Same journal

Neuroimaging and the Epilepsy Journey.

Neuroimaging clinics of North America·2026

Same journal

Clinical Functional Magnetic Resonance Imaging in Epilepsy.

Neuroimaging clinics of North America·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 6, 2025

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

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

Published on: August 16, 2020

Machine Learning Algorithm Validation: From Essentials to Advanced Applications and Implications for Regulatory

Farhad Maleki¹, Nikesh Muthukrishnan¹, Katie Ovens²

¹Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada.

Neuroimaging Clinics of North America

|October 11, 2020

Summary

This summary is machine-generated.

This study details machine learning (ML) model evaluation for healthcare. Rigorous methods ensure ML models are reproducible and generalizable for clinical deployment, improving patient care.

Keywords:

Ability to generalize Artificial intelligence Cross-validation Deep learning Evaluation Machine learning Reproducibility Validation

More Related Videos

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Dec 6, 2025

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

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

Published on: August 16, 2020

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Clinical Decision Support

Background:

Machine learning (ML) models offer potential to enhance healthcare through improved diagnostic speed, accuracy, and treatment planning.
Clinical deployment of ML requires robust methodologies for generalization and reproducibility.

Purpose of the Study:

To outline fundamental concepts and processes for evaluating ML models in healthcare.
To highlight common workflows for ML model development and validation.
To discuss requirements for successful clinical deployment of ML.

Main Methods:

Review of fundamental ML model evaluation concepts.
Description of common ML development and evaluation workflows.
Analysis of requirements for clinical implementation.

Main Results:

Identified key principles for rigorous ML model evaluation.
Illustrated standard workflows for ML model development and validation.
Outlined critical factors for deploying ML in clinical settings.

Conclusions:

Sound methodology in ML model development and evaluation is crucial for clinical translation.
Reproducibility and generalizability are essential for deploying ML in healthcare.
This article provides a foundational guide for ML model evaluation and deployment in clinical practice.