AIMC Topic: Racial Groups
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Acquisition parameters influence AI recognition of race in chest x-rays and mitigating these factors reduces underdiagnosis bias.
Nature communications
Aug 29, 2024
A core motivation for the use of artificial intelligence (AI) in medicine is to reduce existing healthcare disparities. Yet, recent studies have demonstrated two distinct findings: (1) AI models can show performance biases in underserved populations,...
Artificial Intelligence Portrayals in Orthopaedic Surgery: An Analysis of Gender and Racial Diversity Using Text-to-Image Generators.
The Journal of bone and joint surgery. American volume
Jul 18, 2024
BACKGROUND: The increasing accessibility of artificial intelligence (AI) text-to-image generators offers a novel avenue for exploring societal perceptions. The present study assessed AI-generated images to examine the representation of gender and rac...
Fairness in Predicting Cancer Mortality Across Racial Subgroups.
JAMA network open
Jul 1, 2024
IMPORTANCE: Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so th...
Representations and consequences of race in AI systems.
Current opinion in psychology
Jun 22, 2024
Race is directly or indirectly incorporated into many AI systems. These systems, which automate typically human tasks, are used across various domains such as predictive policing, disease detection, government resource allocation, and loan approvals....
Deep Survival Analysis With Latent Clustering and Contrastive Learning.
IEEE journal of biomedical and health informatics
May 6, 2024
Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in sur...
Artificial intelligence in dermatology: advancements and challenges in skin of color.
International journal of dermatology
Mar 6, 2024
Artificial intelligence (AI) uses algorithms and large language models in computers to simulate human-like problem-solving and decision-making. AI programs have recently acquired widespread popularity in the field of dermatology through the applicati...
The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.
AIDS and behavior
Feb 7, 2024
We investigate risk factors for severe COVID-19 in persons living with HIV (PWH), including among racialized PWH, using the U.S. population-sampled National COVID Cohort Collaborative (N3C) data released from January 1, 2020 to October 10, 2022. We d...
Efficient adversarial debiasing with concept activation vector - Medical image case-studies.
Journal of biomedical informatics
Dec 1, 2023
BACKGROUND: A major hurdle for the real time deployment of the AI models is ensuring trustworthiness of these models for the unseen population. More often than not, these complex models are black boxes in which promising results are generated. Howeve...
Implications of predicting race variables from medical images.
Science (New York, N.Y.)
Jul 13, 2023
AI-predicted race variables pose risks and opportunities for studying health disparities.
The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.
PloS one
Feb 18, 2022
Human identification of unknown samples following disaster and mass casualty events is essential, especially to bring closure to family and friends of the deceased. Unfortunately, victim identification is often challenging for forensic investigators ...
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