Cultural Competence

Disentangled contrastive learning for fair graph representations.

Graph Neural Networks (GNNs) play a key role in efficiently learning node representations of graph-s...

Are automated video interviews smart enough? Behavioral modes, reliability, validity, and bias of machine learning cognitive ability assessments.

Automated video interviews (AVIs) that use machine learning (ML) algorithms to assess interviewees a...

Applying a community-engaged participatory machine learning model.

Although predictive algorithms have been described as the definitive solution to bias in health care...

A deep learning-based approach for unbiased kinematic analysis in CNS injury.

Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals ...

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community ...

Patient Diversity and Collaborative Co-Reasoning for Ethical Use of Machine Learning-Driven Decision Support Systems.

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and...

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions.

The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course ...

Perspectives on AI use in medicine: views of the Italian Society of Artificial Intelligence in Medicine.

The first annual meeting of the Italian Society for Artificial Intelligence in Medicine (Società Ita...

Disability Ethics and Education in the Age of Artificial Intelligence: Identifying Ability Bias in ChatGPT and Gemini.

OBJECTIVE: To identify and quantify ability bias in generative artificial intelligence large languag...

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

Handling missing data in clinical prognostic studies is an essential yet challenging task. This stud...

Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer.

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in ...

Mitigating the risk of artificial intelligence bias in cardiovascular care.

Digital health technologies can generate data that can be used to train artificial intelligence (AI)...

Acquisition parameters influence AI recognition of race in chest x-rays and mitigating these factors reduces underdiagnosis bias.

A core motivation for the use of artificial intelligence (AI) in medicine is to reduce existing heal...

The risks and benefits of using AI for diagnosis: beware of unconscious bias.

Understanding AI bias in clinical practice.

Single-Molecule Identification and Quantification of Steviol Glycosides with a Deep Learning-Powered Nanopore Sensor.

Steviol glycosides (SGs) are a class of high-potency noncalorie natural sweeteners made up of a comm...

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The potential for machine learning to answer questions of environmental science, monitoring, and reg...

Health inequities, bias, and artificial intelligence.

Musculoskeletal (MSK) pain leads to significant healthcare utilization, decreased productivity, and ...

Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver.

OBJECTIVE: This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by ...

Zero- and few-shot prompting of generative large language models provides weak assessment of risk of bias in clinical trials.

Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervis...