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Bias01:22

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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...
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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.
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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...
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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...
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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:  
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Understanding Bias in Artificial Intelligence: A Practice Perspective.

Melissa A Davis1, Ona Wu2, Ichiro Ikuta3

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This summary is machine-generated.

Artificial intelligence (AI) in neuroradiology requires careful evaluation for health equity bias. This perspective guides neuroradiologists in assessing AI tools to ensure equitable radiologic care.

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Area of Science:

  • Neuroradiology
  • Artificial Intelligence
  • Health Equity

Background:

  • The American Society of Neuroradiology (ASNR) Diversity and Inclusion Committee hosted a webinar on artificial intelligence (AI) bias in healthcare.
  • Understanding and mitigating bias in AI tools is crucial for ensuring equitable radiologic care.
  • Neuroradiologists must engage with evolving AI technologies to maintain continuous learning and ethical practice.

Purpose of the Study:

  • To distill key concepts from an ASNR webinar on AI bias in neuroradiology.
  • To provide neuroradiologists with a framework for assessing health equity-related bias in AI tools.
  • To explore the impact of AI on equitable radiologic care through clinical workflow examples.

Main Methods:

  • Distillation of key discussion points from an ASNR webinar.
  • Development of a framework for evaluating AI tools for health equity bias.
  • Presentation of clinical workflow implementation examples of AI in neuroradiology.

Main Results:

  • Identified the importance of addressing AI bias for neuroradiologists.
  • Provided insights into developing a framework for assessing AI bias.
  • Highlighted the potential impact of AI on equitable radiologic care.

Conclusions:

  • Neuroradiologists need to actively evaluate AI tools for potential biases.
  • A structured framework is essential for assessing health equity in AI applications.
  • Engaging with AI is vital for advancing equitable practices in radiologic care.