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

Stereotype Content Model02:16

Stereotype Content Model

14.8K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.8K
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

560
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
560
Bias01:22

Bias

4.3K
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...
4.3K
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

90.3K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
90.3K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

431
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
431
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.6K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.

Nature communications·2026
Same author

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction.

bioRxiv : the preprint server for biology·2026
Same author

Applying User-Centered Design to Develop a Prescriber Feedback Tool in Acute Outpatient Care Settings at the Veterans Health Administration.

Applied clinical informatics·2026
Same author

Obesity-Related Metabolites are Associated with Incident Coronary Heart Disease and Respond to Metabolic and Bariatric Surgery.

medRxiv : the preprint server for health sciences·2026
Same author

Reframing AI for Rare Disease Recognition.

Research square·2026
Same author

Reward-Guided Generation Improves the Scientific Utility of Synthetic Biomedical Data.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Aug 5, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.0K

Human-Centered Design to Address Biases in Artificial Intelligence.

You Chen1,2, Ellen Wright Clayton3,4,5, Laurie Lovett Novak1

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.

Journal of Medical Internet Research
|March 24, 2023
PubMed
Summary

Artificial intelligence (AI) can reduce health disparities, but also worsen them if biases aren't addressed. A human-centered approach throughout the AI life cycle is key to ensuring equitable healthcare benefits for all.

Keywords:
AIapplicationartificial intelligencebenefitsbiasesbiomedicalcaredesigndevelopmenthealthhuman-centeredhuman-centered AIpatientresearch

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

404
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.9K

Related Experiment Videos

Last Updated: Aug 5, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

404
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.9K

Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Health Equity

Background:

  • Artificial intelligence (AI) holds promise for reducing health disparities.
  • However, AI can also exacerbate existing inequities if not implemented carefully.
  • Potential biases exist throughout the AI development and deployment lifecycle.

Purpose of the Study:

  • To identify potential biases in the AI life cycle in healthcare.
  • To propose strategies for mitigating these biases.
  • To promote equitable AI implementation for reducing health disparities.

Main Methods:

  • Perspective piece analyzing the AI life cycle stages.
  • Identification of bias points from data collection to feedback integration.
  • Proposal of human-centered AI principles and stakeholder involvement.

Main Results:

  • Biases can manifest at every stage: data collection, annotation, model development, evaluation, deployment, and monitoring.
  • Diverse stakeholder involvement is crucial for identifying and addressing biases.
  • Human-centered AI principles offer a framework for equitable development and use.

Conclusions:

  • Addressing biases in AI is essential for realizing its potential in healthcare.
  • Equitable AI implementation requires proactive mitigation strategies at all lifecycle stages.
  • Human-centered AI can help ensure AI systems reduce, rather than widen, health disparities.