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

Nursing Ethical Principles II01:27

Nursing Ethical Principles II

953
Ethical principles are essential in guiding nurses to fulfill their responsibilities, focusing on the quality of nursing care and decision-making. These principles, including autonomy, beneficence, non-maleficence, justice, and fidelity, shape the ethical framework within healthcare settings.
Consider the following scenario, which illustrates how these principles are applied in the care of Mr. John, a fifty-year-old teacher diagnosed with metastatic liver cancer.
Initially, Mr. John's...
953
Treatment Resistant Cancers02:56

Treatment Resistant Cancers

3.3K
Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.3K
Cancer Therapies02:49

Cancer Therapies

7.6K
Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
7.6K
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
Cancer Survival Analysis01:21

Cancer Survival Analysis

343
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
343
Tumor Immunotherapy01:27

Tumor Immunotherapy

514
Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
514

You might also read

Related Articles

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

Sort by
Same author

Peri-aortic fat to assess cardiovascular aging using an AI-driven radiomic biomarker.

European heart journal·2026
Same author

AI-Based Pathology classifier Predicts Sensitivity to Enzalutamide in Metastatic Hormone-Sensitive Prostate Cancer: A Biomarker Analysis of the ENZAMET Trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Computationally Derived Spatial Immune Signature Identifies Trastuzumab Responders in HER2+ Breast Cancer: NSABP B-41 Clinical Trial Validation.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

AID-FGS: Artificial intelligence-enabled diagnosis of female genital schistosomiasis: Preliminary findings.

PLOS digital health·2026
Same author

STAR (stroma-tumor AI risk) assessment: association of AI-derived tumor-stroma proportion with patient survival provides added prognostic value beyond KELIM in epithelial ovarian cancer.

BJC reports·2026
Same author

Integrating intramuscular fat radiomics with hamstrings-to-quadriceps structure and function ratios to predict future hamstring strain injury.

PLOS digital health·2025

Related Experiment Video

Updated: Jun 24, 2025

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer
08:26

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer

Published on: February 23, 2024

2.0K

Towards equitable AI in oncology.

Vidya Sankar Viswanathan1, Vani Parmar2, Anant Madabhushi3,4

  • 1Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.

Nature Reviews. Clinical Oncology
|June 7, 2024
PubMed
Summary

Artificial intelligence (AI) can transform cancer care, but its benefits are unevenly distributed. Developing equitable AI tools is crucial for diverse patient populations globally, especially in low-income countries.

More Related Videos

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.3K
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.4K

Related Experiment Videos

Last Updated: Jun 24, 2025

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer
08:26

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer

Published on: February 23, 2024

2.0K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.3K
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.4K

Area of Science:

  • Oncology
  • Medical Informatics
  • Health Equity

Background:

  • Artificial intelligence (AI) shows promise for revolutionizing clinical oncology, improving cancer detection, risk assessment, and personalized treatment.
  • Current AI benefits are inequitably distributed, favoring specific geographical locations and populations.
  • A significant gap exists in AI tool accessibility and accuracy across diverse patient groups.

Purpose of the Study:

  • To highlight the need for equitable artificial intelligence (AI) tool development in clinical oncology.
  • To discuss challenges and solutions for achieving AI equity in diverse patient populations, including those in low- and middle-income countries.
  • To address the risks of bias and inequity in AI development and deployment.

Main Methods:

  • Discussion of existing inequities in AI distribution and access.
  • Analysis of challenges in developing equitable AI, including data representation and validation methods.
  • Focus on model approaches, dataset curation, and contextual bias in AI development.

Main Results:

  • AI's potential in oncology is significant but currently limited by inequitable distribution.
  • Diverse populations, particularly in low-income countries, are underserved by current AI advancements.
  • Historical data limitations and inadequate validation methods exacerbate AI inequities.

Conclusions:

  • Fostering equitable AI development is essential for realizing its full potential in global oncology.
  • Addressing data representation, validation, and contextual bias is key to achieving AI equity.
  • Collaborative efforts are needed to ensure AI tools benefit all patient populations, regardless of location or socioeconomic status.