AIMC Topic: Minority Groups
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Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students.
PLoS computational biology
Jan 31, 2022
Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The U...
Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients.
Molecular psychiatry
Jan 8, 2022
Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to ...
Ensemble machine learning of factors influencing COVID-19 across US counties.
Scientific reports
Jun 3, 2021
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and...
The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations.
Journal of medical Internet research
Feb 10, 2021
There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities-foreign-born, immigrant, refugee, and culturally marginalized-are at increased ri...
The Emerging Hazard of AI-Related Health Care Discrimination.
The Hastings Center report
Dec 14, 2020
Artificial intelligence holds great promise for improved health-care outcomes. But it also poses substantial new hazards, including algorithmic discrimination. For example, an algorithm used to identify candidates for beneficial "high risk care manag...
A Novel Use of Artificial Intelligence to Examine Diversity and Hospital Performance.
The Journal of surgical research
Oct 21, 2020
BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. F...
Word embeddings quantify 100 years of gender and ethnic stereotypes.
Proceedings of the National Academy of Sciences of the United States of America
Apr 3, 2018
Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we de...
The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes.
Medical decision making : an international journal of the Society for Medical Decision Making
Dec 1, 2014
OBJECTIVE: To evaluate the impact of the synthetic minority oversampling technique (SMOTE) on the performance of probabilistic neural network (PNN), naïve Bayes (NB), and decision tree (DT) classifiers for predicting diabetes in a prospective cohort ...
Increasing the ethnic diversity of senior leadership within the English National Health Service: using an artificial intelligence approach to evaluate inclusive recruitment strategies in hospital settings.
Human resources for health
May 22, 2025
BACKGROUND: The English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) representation in senior leadership roles remains limited. To address this, a large multi-hospital acute N...
Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models ma...
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