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

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

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...

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:

The Availability Heuristic

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

You might also read

Related Articles

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

Sort by

Same author

Continuous Glucose Monitoring and Personalized Nutrition in Type 2 Diabetes - A Scoping Review.

Studies in health technology and informatics·2026

Same authorSame journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026

Same authorSame journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026

Same author

Sukunn: Bridging Spiritual Heritage and Modern Digital Mental Health.

Studies in health technology and informatics·2026

Same author

Digital mental health needs a purpose-driven approach.

Nature human behaviour·2026

Same author

EchoNet++: A multilingual soccer match audio commentary dataset.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Sep 12, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Biased AI: A Case for Positive Bias in Healthcare AI.

Hurmat Ali Shah¹, Zain Ul Abideen Tariq¹, Marco Agus¹

¹College of Science and Engineering, Hamad bin Khalifa University, Qatar.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This study redefines artificial intelligence (AI) bias as a tool for equity, proposing a framework to correct healthcare disparities and improve outcomes for marginalized groups using purpose-driven AI.

Keywords:

AI AI in healthcare bias in AI large language models

More Related Videos

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

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

Published on: June 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Sep 12, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

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

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

Published on: June 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Healthcare technology
Artificial intelligence ethics
Health equity

Background:

Artificial intelligence (AI) bias in healthcare often perpetuates systemic inequities.
Existing algorithms may underestimate risks for Black patients or fail in diagnosing dark-skinned individuals.
These biases reinforce disparities in medical care and outcomes.

Purpose of the Study:

To propose an innovative framework for repurposing AI bias as a tool for addressing structural injustices.
To leverage AI bias to improve health outcomes for underrepresented and marginalized groups.
To redefine AI bias as a mechanism for enhancing fairness in healthcare.

Main Methods:

Conducting thorough bias analysis within AI systems.
Curating diverse and representative datasets for AI training.
Fine-tuning AI models to align with specific fairness objectives and equity goals.

Main Results:

Demonstrating the potential of purpose-driven bias to correct systemic healthcare disparities.
Showing how AI can be intentionally utilized to enhance fairness in diagnostics and medical interventions.
Highlighting the capability of "biased AI" to drive more inclusive healthcare practices.

Conclusions:

AI bias, when intentionally repurposed, can be a powerful tool for achieving health equity.
The proposed framework offers a novel approach to mitigate existing biases and promote fairness.
This strategy can lead to more equitable healthcare systems and improved patient outcomes for all groups.