AIMC Topic: Black or African American
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Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.
Artificial intelligence in medicine
Jun 1, 2025
A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical ...
"What Did You Say, ChatGPT?" The Use of AI in Black Women's HIV Self-Education: An Inductive Qualitative Data Analysis.
The Journal of the Association of Nurses in AIDS Care : JANAC
May 14, 2025
The emergence of widely accessible artificial intelligence (AI) chatbots such as ChatGPT presents unique opportunities and challenges in public health self-education. This study examined simulations with ChatGPT for its use in public education of sex...
Assessing Algorithmic Fairness With a Multimodal Artificial Intelligence Model in Men of African and Non-African Origin on NRG Oncology Prostate Cancer Phase III Trials.
JCO clinical cancer informatics
May 1, 2025
PURPOSE: Artificial intelligence (AI) tools could improve clinical decision making or exacerbate inequities because of bias. African American (AA) men reportedly have a worse prognosis for prostate cancer (PCa) and are underrepresented in the develop...
Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the National Quality Improvement Program Database Using Machine Learning.
Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
May 1, 2025
PURPOSE: To develop machine learning models using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database to predict prolonged operative time (POT) for rotator cuff repair (RCR), as well as use the trained machine l...
Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.
Studies in health technology and informatics
Apr 8, 2025
We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to recruit African American informal caregivers for clinical trials and general health disparity researc...
Applying Data Mining to Predict Perceived Benefits Risks of Robotics at Home for Dementia Caregiving Among African American Families.
Studies in health technology and informatics
Apr 8, 2025
We used data mining to predict the attitudes of 527 caregivers towards the pros and cons of using robotics and artificial intelligence (AI) for dementia care in African American families, with a focus on family-level factors. African American family ...
Comparing Emotional Valence from Human Quantitative Ratings and Qualitative Narrative Data on Using Artificial Intelligence to Reduce Caregiving Disparity.
Studies in health technology and informatics
Apr 8, 2025
We compared emotional valence scores as determined via machine vs human ratings from a survey conducted from April to May 2024 on perceived attitudes on the use of artificial intelligence (AI) for African American family caregivers of persons with Al...
Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.
JNCI cancer spectrum
Mar 3, 2025
BACKGROUND: We use real-world data to develop a lung cancer screening (LCS) eligibility mechanism that is both accurate and free from racial bias.
Fair prediction of 2-year stroke risk in patients with atrial fibrillation.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2024
OBJECTIVE: This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.
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