Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Bias in Epidemiological Studies

Bias in Epidemiological Studies

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:

Bias

Bias

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...

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

Motivational Bias

Motivational Bias

Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...

You might also read

Related Articles

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

Sort by

Same author

High Cost of Cancer Medicines in the United States: Opportunities for Policy Reforms.

JCO oncology practice·2026

Same author

Review Duration and Therapeutic Value of Cancer Drugs Granted Priority Versus Nonpriority Review in the United States, European Union, and Switzerland (2010-2024).

JCO oncology practice·2026

Same author

Quantification of Age and Sex Ratio Differences between Trial and Target Population for New Drugs.

Clinical pharmacology and therapeutics·2026

Same author

Therapeutic benefit of the most expensive drugs covered by Medicare and Medicaid.

Journal of pharmaceutical policy and practice·2025

Same author

Generic competition and price developments in the USA, Germany and Switzerland (2007-2023): a longitudinal observational study.

BMJ public health·2025

Same author

Differences Between Trial Populations and Approved Label Populations of New Drugs in the United States and Europe (2012 to 2023) : A Cross-Sectional Study.

Annals of internal medicine·2025

Same journal

Paired longitudinal tear proteomics reveals persistent ocular surface dysregulation after teprotumumab therapy in thyroid eye disease.

Communications medicine·2026

Same journal

In vitro development of the Autonomous Colonoscope Robot System (ACRS) for fully automated colonoscope insertion.

Communications medicine·2026

Same journal

Dynamic contrast-enhanced ultrasound predicts microsphere biodistribution in real-time during transarterial radioembolization.

Communications medicine·2026

Same journal

Circulating monocyte gene expression profiles associated with cardiac remodeling and incident heart failure in the Multi-Ethnic Study of Atherosclerosis.

Communications medicine·2026

Same journal

Impact of methicillin resistance on mortality in Staphylococcus aureus endocarditis: a systematic review and meta-analysis.

Communications medicine·2026

Same journal

Clinical benefits of tirzepatide in patients with steatotic liver disease and cardiometabolic dysfunction.

Communications medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 20, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Mitigating bias in machine learning for medicine.

Kerstin N Vokinger^1,2, Stefan Feuerriegel^3,4, Aaron S Kesselheim²

¹Institute of Law, University of Zurich, Zurich, Switzerland.

Communications Medicine

|September 15, 2021

Summary

This summary is machine-generated.

Bias in medical machine learning can harm patient care. This study outlines strategies to reduce bias throughout the development of these AI systems, ensuring safer clinical applications.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Oct 20, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Medical Artificial Intelligence (AI)
Machine Learning (ML) in Healthcare
Clinical Decision Support Systems

Background:

Machine learning systems are increasingly used in medical applications.
Various sources of bias can negatively impact ML model performance.
Such biases can lead to disparities in clinical care and affect patient outcomes.

Purpose of the Study:

To identify and discuss sources of bias in medical machine learning.
To propose actionable solutions for mitigating bias in ML systems for healthcare.
To enhance the reliability and fairness of AI in clinical settings.

Main Methods:

Review of common bias types in ML development.
Analysis of bias mitigation strategies at different development stages.
Discussion of best practices for equitable AI in medicine.

Main Results:

Bias can be introduced during data collection, model training, and deployment.
Proactive bias detection and correction are crucial.
Implementing fairness-aware algorithms and diverse datasets can reduce bias.

Conclusions:

Addressing bias is essential for the safe and effective use of ML in medicine.
Mitigation strategies should be integrated throughout the ML lifecycle.
Reducing bias promotes equitable healthcare delivery through AI.