AIMC Topic: Bias

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Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.

Magnetic resonance in medicine Dec 19, 2022
PURPOSE: The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy an...
Algorithms Bias Deep Learning
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Unconscious Other's Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others' Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI.

Sensors (Basel, Switzerland) Dec 17, 2022
Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) c...
Cognition Emotions Artificial Intelligence Attitude Bias Humans
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Population codes enable learning from few examples by shaping inductive bias.

eLife Dec 16, 2022
Learning from a limited number of experiences requires suitable inductive biases. To identify how inductive biases are implemented in and shaped by neural codes, we analyze sample-efficient learning of arbitrary stimulus-response maps from arbitrary ...
Bias Brain Mice Artificial Intelligence Animals
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A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health.

Perspectives on psychological science : a journal of the Association for Psychological Science Dec 9, 2022
Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experie...
Humans Artificial Intelligence Bias Mental Health Awareness Technology
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Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.

Journal of neurointerventional surgery Nov 23, 2022
BACKGROUND: This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).
Humans Deep Learning Sensitivity and Specificity Bayes Theorem Hematoma, Subdural Bias
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Algorithmic bias in health care: Opportunities for nurses to improve equality in the age of artificial intelligence.

Nursing outlook Nov 14, 2022
Artificial Intelligence Humans Delivery of Health Care Bias
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A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

International journal of environmental research and public health Nov 12, 2022
Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodolo...
Causality Bias Databases, Factual Machine Learning Humans Research Design
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Evaluation of machine learning methods for covariate data imputation in pharmacometrics.

CPT: pharmacometrics & systems pharmacology Nov 8, 2022
Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, suc...
Data Interpretation, Statistical Bias Machine Learning Computer Simulation Humans
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Preventing Artificial Intelligence in Medical Imaging From Perpetuating Health Care Biases and Disparities.

Journal of the American College of Radiology : JACR Oct 5, 2022
Diagnostic Imaging Bias Artificial Intelligence Delivery of Health Care
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Algorithmic fairness in computational medicine.

EBioMedicine Sep 6, 2022
Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, wh...
Decision Making Machine Learning Humans Clinical Decision-Making Bias
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