AIMC Topic: Bias

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Large Language Model-Assisted Risk-of-Bias Assessment in Randomized Controlled Trials Using the Revised Risk-of-Bias Tool: Usability Study.

Journal of medical Internet research
BACKGROUND: The revised Risk-of-Bias tool (RoB2) overcomes the limitations of its predecessor but introduces new implementation challenges. Studies demonstrate low interrater reliability and substantial time requirements for RoB2 implementation. Larg...

Tailoring task arithmetic to address bias in models trained on multi-institutional datasets.

Journal of biomedical informatics
OBJECTIVE: Multi-institutional datasets are widely used for machine learning from clinical data, to increase dataset size and improve generalization. However, deep learning models in particular may learn to recognize the source of a data element, lea...

Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO via machine learning.

Environmental pollution (Barking, Essex : 1987)
The Geostationary Environment Monitoring Spectrometer (GEMS) has revolutionized air quality monitoring with hourly resolution from geostationary Earth orbit (GEO). However, satellite-derived air quality data often face limitations and biases due to m...

Evaluating and mitigating bias in AI-based medical text generation.

Nature computational science
Artificial intelligence (AI) systems, particularly those based on deep learning models, have increasingly achieved expert-level performance in medical applications. However, there is growing concern that such AI systems may reflect and amplify human ...

Detecting implicit biases of large language models with Bayesian hypothesis testing.

Scientific reports
Despite the remarkable performance of large language models (LLMs), such as generative pre-trained Transformers (GPTs), across various tasks, they often perpetuate social biases and stereotypes embedded in their training data. In this paper, we intro...

Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance acr...

Chatbots for conducting systematic reviews in pediatric dentistry.

Journal of dentistry
OBJECTIVES: The performance of chatbots for discrete steps of a systematic review (SR) on artificial intelligence (AI) in pediatric dentistry was evaluated.

Demographic bias of expert-level vision-language foundation models in medical imaging.

Science advances
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit trainin...

Artificial Intelligence Algorithms, Bias, and Innovation: Implications for Social Work.

Journal of evidence-based social work (2019)
PURPOSE: Artificial Intelligence (AI) technologies are rapidly expanding across diverse contexts. As the reach of AI continues to grow, there is a need to examine student perspectives on the increasing prevalence of AI and AI-based practice approache...

Biases in machine-learning models of human single-cell data.

Nature cell biology
Recent machine-learning (ML)-based advances in single-cell data science have enabled the stratification of human tissue donors at single-cell resolution, promising to provide valuable diagnostic and prognostic insights. However, such insights are sus...