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

Ethical Issues01:27

Ethical Issues

1.3K
Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
1.3K
Nursing Ethical Principles II01:27

Nursing Ethical Principles II

1.4K
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...
1.4K
Standards of Care I01:22

Standards of Care I

777
Federal statutes profoundly impact nursing practice, providing critical guidelines to ensure patient care is equitable, accessible, and of the highest quality. The following laws address distinct aspects of healthcare provision and patient rights:
777
Randomized Experiments01:13

Randomized Experiments

8.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.1K
Bias01:22

Bias

5.8K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
5.8K
Ethics and Bioethics01:22

Ethics and Bioethics

1.7K
Ethics is a philosophical study of moral actions. Ethics attempts to determine what is valuable for individuals and society. It examines the rational justification of moral judgments and analyzes what is morally just, fair, and right. Bioethics is a sub-discipline of applied ethics that analyzes the philosophical, social, and legal issues in life sciences and medicine. Ethical theories serve as a foundation for decision-making and represent the viewpoints from which people seek direction. They...
1.7K

You might also read

Related Articles

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

Sort by
Same author

LLMs achieve adult human performance on higher-order theory of mind tasks.

Frontiers in human neuroscience·2026
Same author

Safety and efficacy of 6% hydroxyethyl starch in patients undergoing major surgery: The randomised controlled PHOENICS trial.

European journal of anaesthesiology·2025
Same author

Predicting and explaining with machine learning models: Social science as a touchstone.

Studies in history and philosophy of science·2024
Same author

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study.

EClinicalMedicine·2024
Same author

A paradigm shift?-On the ethics of medical large language models.

Bioethics·2024
Same author

Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice.

The Journal of medicine and philosophy·2023
Same journal

Sentience. Not Necessarily a Problem?

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees·2026
Same journal

No Need to Feel.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees·2026
Same journal

The One Health Paradigm and Wild Animal Welfare Science.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees·2026
Same journal

From Mollusks to Machines: An Ethical Framework Focused on the Urgency of Extreme Suffering.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees·2026
Same journal

Sentience and Why It Matters.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees·2026
Same journal

The Wrong Motives for Potentially Harming a Being.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees·2026
See all related articles

Related Experiment Video

Updated: Oct 6, 2025

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

23.9K

On Algorithmic Fairness in Medical Practice.

Thomas Grote1, Geoff Keeling2

  • 1Ethics and Philosophy Lab, Cluster of Excellence: Machine Learning: New Perspectives for Science, University of Tübingen, Tübingen, Germany.

Cambridge Quarterly of Healthcare Ethics : CQ : the International Journal of Healthcare Ethics Committees
|January 20, 2022
PubMed
Summary
This summary is machine-generated.

Algorithmic bias in medicine may worsen health disparities. This study clarifies how bias arises and its ethical implications for healthcare fairness and justice.

Keywords:
algorithmic biasdiscriminationfairnessmachine learningmedical practice

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
08:01

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

4.8K

Related Experiment Videos

Last Updated: Oct 6, 2025

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

23.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
08:01

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

4.8K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Equity

Background:

  • Machine learning (ML) enhances medical assessment, diagnosis, and treatment.
  • Algorithmic bias in ML tools risks perpetuating or worsening health inequalities.
  • Understanding bias sources is crucial for equitable healthcare.

Purpose of the Study:

  • To precisely define the different ways algorithmic bias can manifest in medical applications.
  • To clarify the ethical significance of these biases concerning justice and fairness in healthcare.
  • To lay the groundwork for a comprehensive framework of algorithmic bias in medicine.

Main Methods:

  • Conceptual analysis of algorithmic bias in medical contexts.
  • Ethical framework development for assessing bias in healthcare AI.
  • Literature review on machine learning applications and health disparities.

Main Results:

  • Identified multiple pathways through which algorithmic bias can emerge in medical AI.
  • Established the normative relevance of specific bias types for healthcare justice.
  • Provided foundational concepts for analyzing and mitigating bias in medical algorithms.

Conclusions:

  • Addressing algorithmic bias is essential for achieving fairness and justice in AI-driven healthcare.
  • A clear understanding of bias mechanisms is a prerequisite for developing equitable medical technologies.
  • This work offers a framework for future research on ethical AI in medicine.