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Bias

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Bias in artificial intelligence in vascular surgery.

Seminars in vascular surgery
Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning models to learn without a priori assumption, or without prior learning, to connect seemingly unrela...

Hierarchical Bias Mitigation for Semi-Supervised Medical Image Classification.

IEEE transactions on medical imaging
Semi-supervised learning (SSL) has demonstrated remarkable advances on medical image classification, by harvesting beneficial knowledge from abundant unlabeled samples. The pseudo labeling dominates current SSL approaches, however, it suffers from in...

"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation.

Journal of the American College of Radiology : JACR
Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various subgroups limit the usefulness of AI in improving patients' lives. Many ...

Investigating the impact of cognitive biases in radiologists' image interpretation: A scoping review.

European journal of radiology
RATIONALE AND OBJECTIVE: Image interpretation is a fundamental aspect of radiology. The treatment and management of patients relies on accurate and timely imaging diagnosis. However, errors in radiological reports can negatively impact on patient hea...

The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models.

Perspectives on psychological science : a journal of the Association for Psychological Science
Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tool...

Understanding Biases and Disparities in Radiology AI Datasets: A Review.

Journal of the American College of Radiology : JACR
Artificial intelligence (AI) continues to show great potential in disease detection and diagnosis on medical imaging with increasingly high accuracy. An important component of AI model creation is dataset development for training, validation, and tes...

Mitigating bias in AI at the point of care.

Science (New York, N.Y.)
Promoting equity in AI in health care requires addressing biases at cli nical implementation.

Large language models encode clinical knowledge.

Nature
Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to a...

How AI can distort human beliefs.

Science (New York, N.Y.)
Models can convey biases and false information to users.

Artificial Intelligence Bias in Health Care: Web-Based Survey.

Journal of medical Internet research
BACKGROUND: Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algo...