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

You might also read

Related Articles

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

Sort by
Same author

Otolith dysfunction in recurrent benign paroxysmal positional vertigo after mild traumatic brain injury.

Acta oto-laryngologica·2019
Same author

Dark Current Reduction Strategy via a Layer-By-Layer Solution Process for a High-Performance All-Polymer Photodetector.

ACS applied materials & interfaces·2019
Same author

Combination of ozonation and electrolysis process to enhance elimination of thirty structurally diverse pharmaceuticals in aqueous solution.

Journal of hazardous materials·2019
Same author

Long noncoding RNA <i>ANRIL</i> regulates endothelial cell activities associated with coronary artery disease by up-regulating <i>CLIP1</i>, <i>EZR</i>, and <i>LYVE1</i> genes.

The Journal of biological chemistry·2019
Same author

Degradation of Ofloxacin by Perylene Diimide Supramolecular Nanofiber Sunlight-Driven Photocatalysis.

Environmental science & technology·2019
Same author

Gene expression profiling reveals differential patterns between microcystic congenital cystic adenomatoid malformation and congenital lobar emphysema.

Early human development·2018
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 13, 2025

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.8K

Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.

Yuanqing Yang1,2, Kai Sun1,3, Yanhua Gao4

  • 1Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China.

Diagnostics (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence in pathology (AIP) shows promise but faces challenges in clinical settings. Robust data preparation, standardization, and whole slide image (WSI) analysis are key to improving AIP

Keywords:
artificial intelligence in pathologyclinical-gradedata preparationdeep learning

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:08

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

69

Related Experiment Videos

Last Updated: Jul 13, 2025

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:08

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

69

Area of Science:

  • Digital pathology
  • Artificial intelligence in pathology (AIP)

Background:

  • Pathology relies on experienced pathologists for diagnosis.
  • Artificial intelligence in pathology (AIP) is emerging to improve accuracy and efficiency.
  • Clinical application of AIP faces challenges in replicating lab performance.

Purpose of the Study:

  • Review AIP studies (Jan 2017-Feb 2022) from PubMed (118 studies).
  • Analyze data preparation methods for AIP.
  • Identify challenges and strategies for enhancing AIP's clinical performance.

Main Methods:

  • Systematic review of 118 AIP studies.
  • Analysis of data acquisition, cleaning, screening, digitization, annotation, and validation.
  • Investigation of factors affecting AIP performance reproducibility.

Main Results:

  • Data preparation is crucial for AIP robustness.
  • Key factors include representative data, quality control, annotation, and data volume.
  • Digital pathology, data standardization, and WSI-based weakly supervised learning are effective strategies.

Conclusions:

  • Reproducibility of AIP performance depends on representative data, adequate labeling, and multi-center consistency.
  • Digital pathology and WSI-based weakly supervised learning are vital for clinical-grade AIP.
  • Standardization and robust data practices are essential for successful clinical integration of AIP.