Surveys

Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.

PURPOSE: Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a true predictor of differential care given an absence of real-world studies. We conducted the first real-world pilot study of provider implicit bias by ...

Developing and Improving Personality Inventories Using Generative Artificial Intelligence: The Psychometric Properties of a Short HEXACO Scale Developed Using ChatGPT 4.0.

In the current study, we investigated the utility of generative AI for survey development and improvement. To do so, we generated a 24-item HEXACO personality inventory using ChatGPT 4.0, the ChatGPT HEXACO inventory (CHI), and investigated whether ChatGPT could modify the CHI to either improve its internal consistency or its content validity. Additionally, we compared the psychometric properties ...

Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis.

Enhancing patient comprehension of their health is crucial in improving health outcomes. The integration of artificial intelligence (AI) in distilling...

Development of a short form of the Geriatric Depression Scale-30 based on item response theory and the RiskSLIM algorithm.

Recently, methods of quickly and accurately screening for geriatric depression have attracted substantial attention. Short forms of the 30-item Geriat...

A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical present...

Deep Learning-Enabled Rapid Metabolic Decoding of Small Extracellular Vesicles via Dual-Use Mass Spectroscopy Chip Array.

The increasing focus of small extracellular vesicles (sEVs) in liquid biopsy has created a significant demand for streamlined improvements in sEV isol...

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

BACKGROUND: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as ...

Biosecurity measures reducing spp. and hepatitis E virus prevalence in pig farms-a systematic review and meta-analysis.

spp. and hepatitis E virus (HEV) are significant foodborne zoonotic pathogens that impact the health of livestock, farmers, and the general public. T...

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study.

BACKGROUND: Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification pro...

The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.

INTRODUCTION: As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfa...

The anesthesiologist's guide to critically assessing machine learning research: a narrative review.

Artificial Intelligence (AI), especially Machine Learning (ML), has developed systems capable of performing tasks that require human intelligence. In ...

A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers.

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI developmen...

Policy brief: Improving national vaccination decision-making through data.

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-di...

Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale.

INTRODUCTION: Missing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Variou...

Ethical and Bias Considerations in Artificial Intelligence/Machine Learning.

As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI mo...

Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery.

It stands to reason that the amount and the quality of data are of key importance for setting up accurate artificial intelligence (AI)-driven models. ...

Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of impro...

Concordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.

In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and persona...

Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data.

BACKGROUND: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown source...

A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications.

Collaborative intelligence (CI) involves human-machine interactions and is deemed safety-critical because their reliable interactions are crucial in p...