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Bias01:22

Bias

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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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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...
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Ethical and Bias Considerations in Artificial Intelligence/Machine Learning.

Matthew G Hanna1, Liron Pantanowitz1, Brian Jackson2

  • 1Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, Pittsburgh, Pennsylvania.

Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc
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PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) offer medical advancements but carry ethical risks. Careful evaluation of AI-ML models is crucial to ensure fairness and prevent bias in healthcare applications.

Keywords:
artificial intelligencebiascomputational pathologyethicsmachine learningpathology

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Pathology

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into medical practice.
  • These technologies show significant capabilities in areas like image recognition and predictive analytics.
  • However, their deployment raises ethical concerns regarding potential biases.

Purpose of the Study:

  • To scrutinize the ethical implications and potential biases of AI and ML models in pathology and medicine.
  • To highlight the importance of addressing bias in AI-ML systems for equitable healthcare.
  • To provide a comprehensive overview of ethical and bias considerations in medical AI-ML applications.

Main Methods:

  • Review of ethical considerations and bias in AI-ML systems within the medical and pathology domains.
  • Categorization of bias sources into data bias, development bias, and interaction bias.
  • Discussion of factors contributing to bias, including training data, algorithms, and clinical practice variations.

Main Results:

  • AI-ML models, while powerful, can inadvertently produce unfair or detrimental outcomes due to various biases.
  • Bias can stem from training data, algorithmic design, feature selection, and clinical practice variability.
  • Ethical concerns are paramount during the development and clinical deployment of AI-ML in medicine.

Conclusions:

  • A comprehensive evaluation process is essential for AI-ML systems, from development to deployment.
  • Addressing bias is critical to ensure AI-ML tools are fair, transparent, and beneficial to all patients.
  • Ongoing scrutiny of ethics and bias is necessary for the responsible integration of AI-ML in pathology and medicine.