AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Bias

Showing 61 to 70 of 298 articles

Clear Filters

Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology.

The Canadian journal of cardiology
In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. In this review we explore the complex issue of data bias, specifically addressing those enc...

Bias-reduced neural networks for parameter estimation in quantitative MRI.

Magnetic resonance in medicine
PURPOSE: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.

A survey of recent methods for addressing AI fairness and bias in biomedicine.

Journal of biomedical informatics
OBJECTIVES: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recogni...

Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

Accident; analysis and prevention
The existing methodologies for allocating highway safety improvement funding closely rely on the utilization of crash prediction models. Specifically, these models produce predictions that estimate future crash hazard levels in different geographical...

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

Journal of biomedical informatics
OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different dise...

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

Gynecologic oncology
OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

BMC medical informatics and decision making
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better ...

Features in Backgrounds of Microscopy Images Introduce Biases in Machine Learning Analyses.

Journal of pharmaceutical sciences
Subvisible particles may be encountered throughout the processing of therapeutic protein formulations. Flow imaging microscopy (FIM) and backgrounded membrane imaging (BMI) are techniques commonly used to record digital images of these particles, whi...

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in...