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

Genomics02:02

Genomics

36.3K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.3K
Tumor Progression02:07

Tumor Progression

6.3K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
6.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
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...
53
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Strengthening Biomarker Research in Canadian Cancer Clinical Trials: A Pathology-Focused White Paper.

Current oncology (Toronto, Ont.)·2026
Same author

Comparative proteomic analysis of cerebrospinal fluid and brain tissue using Orbitrap Astral and timsTOF Pro 2 platforms with data-independent acquisition.

Diagnosis (Berlin, Germany)·2026
Same author

Eosinophilic Pattern of Chromophobe Renal Cell Carcinoma is More Aggressive Than the Classic Pattern: A Comprehensive Morphologic and Genomic Analysis.

The American journal of surgical pathology·2026
Same author

Revisiting Reconstruction-based Anomaly Detection for Whole Slide Image.

IEEE transactions on medical imaging·2026
Same author

Validation of human kallikrein 6 in the cerebrospinal fluid of patients with progressive and non-progressive alzheimer's disease: correlation with other biomarkers.

Clinical proteomics·2026
Same author

Clinical-Grade Interpretable Artificial Intelligence Tool for Automated Detection of Lymph Node Metastasis in Prostate Cancer.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2025
Same journal

Artificial general intelligence and the clinical laboratory: a paradigm shift toward Lab 2.0.

Clinical chemistry and laboratory medicine·2026
Same journal

Addressing property and unit discordance in laboratory interoperability: the role of NPU as a governance layer.

Clinical chemistry and laboratory medicine·2026
Same journal

Ethical evaluation of reflex and reflective testing in conformity with ISO 15189:2022 at the value-based laboratory medicine era.

Clinical chemistry and laboratory medicine·2026
Same journal

Emerging blood-based diagnostic strategies for tuberculosis.

Clinical chemistry and laboratory medicine·2026
Same journal

Homocitrulline and 3-year mortality in older hospitalised adults: an exploratory study.

Clinical chemistry and laboratory medicine·2026
Same journal

Impact of age partitioning on classification discordance in pediatric ferritin reference intervals.

Clinical chemistry and laboratory medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.0K

Computational pathology: an evolving concept.

Ioannis Prassas1,2, Blaise Clarke1,2, Timothy Youssef1

  • 1Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada.

Clinical Chemistry and Laboratory Medicine
|April 22, 2024
PubMed
Summary
This summary is machine-generated.

Computational pathology (CP) and artificial intelligence (AI) are shifting towards computer-assisted diagnostics, supporting pathologists rather than replacing them. Wider clinical adoption requires addressing performance limitations and regulatory hurdles through collaboration.

Keywords:
AI in healthcarecomputational pathologydigital pathologymachine learning

More Related Videos

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

890

Related Experiment Videos

Last Updated: Jun 28, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.0K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

890

Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Pathology informatics

Background:

  • Initial enthusiasm for AI in pathology focused on fully automated diagnostics.
  • Current machine learning models lack the performance for standalone diagnostic decisions.
  • Legal and regulatory complexities also impede full automation.

Purpose of the Study:

  • To explore practical aspects of computational pathology from a pathologist's viewpoint.
  • To identify potential applications and limitations of CP in clinical settings.
  • To outline steps, obstacles, and solutions for clinical implementation of CP.

Main Methods:

  • Review of current state and potential of computational pathology.
  • Analysis of pathologist's perspective on AI integration.
  • Discussion of clinical implementation challenges and collaborative solutions.

Main Results:

  • CP offers enhanced diagnostic precision, prognostic information, and time savings.
  • Key limitations hindering wider clinical adoption include performance and regulatory issues.
  • A collaborative approach involving academia, industry, and regulators is crucial.

Conclusions:

  • Computational pathology is evolving into a computer-assisted diagnostic model.
  • Successful clinical integration requires overcoming technical and regulatory challenges.
  • Broad collaboration is essential for advancing CP in healthcare settings.