Cultural Competence

Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression

Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood-level factors significantly influence PND risk, particularly among women of color, but current machine learning models using electronic medical records (EMRs) rarely incorporate neighborhood characteristics. To determine whether integrating neighbor...

Large Language Models for Sentiment Analysis in Healthcare: A Systematic Review Protocol

Large language models (LLMs) have emerged as powerful tools for sentiment analysis in healthcare, offering potential advantages in capturing contextual information and semantic relationships in complex medical text. Healthcare sentiment analysis presents unique challenges due to domain-specific terminology, privacy regulations, and the nuanced nature of patient experiences. This systematic review ...

AI-Powered Social Robots for Addressing Loneliness: A Systematic Review Protocol

Loneliness is a significant public health concern that affects millions of people worldwide, particularly older adults. With advancements in artificia...

Machine Learning Identifies Microbiome and Clinical Predictors of Sustained Weight Loss Following Prolonged Fasting

Prolonged fasting may benefit metabolic health, but data in healthy individuals remain limited. We performed a randomized, waitlist-controlled study (...

From Keywords to Context: Bridging Expert Insight and Language Models for Multidimensional Sleep Health Classification in Clinical Notes

Accurate detection of multidimensional sleep health (MSH) information from electronic health records (EHRs) is critical for improving clinical decisio...

Mindfulness-Based Interventions using Artificial Intelligence: A Systematic Review Protocol

Mindfulness-based interventions (MBIs) have gained significant recognition as effective approaches for promoting mental health and well-being. With ra...

Lack of children in public medical imaging data points to growing age bias in biomedical AI

Artificial intelligence (AI) is rapidly transforming healthcare, but its benefits are not reaching all patients equally. Children remain overlooked wi...

Barriers and Facilitators to the Implementation of Artificial Intelligence Enabled Diabetes Interventions in Lower-Middle-Income Countries: A Systematic Review Protocol

Diabetes represents an emerging global health crisis, with lower-middle-income countries experiencing a fast growth in prevalence. Diabetes care in th...

Evaluating the performance and potential bias of predictive models for detection of transthyretin cardiac amyloidosis

Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to the significant morbidity of the condition, especially in the ...

LLM Reasoning Does Not Protect Against Clinical Cognitive Biases - An Evaluation Using BiasMedQA

Cognitive biases are an important source of clinical errors. Large language models (LLMs) have emerged as promising tools to support clinical decision...

Conversational AI in Therapy: Current Applications and Future Directions in Mental Health Support

This paper delivers a rigorous mixed-methods synthesis of conversational AI applications in mental health therapy, analyzing 47 randomized controlled ...

A Survey on Optimization and Machine Learning-Based Fair Decision Making in Healthcare

The unintended biases introduced by optimization and machine learning (ML) models are a topic of great interest to medical researchers and professiona...

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study

Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...

Machine Learning for Paediatric Related Decision Support in Emergency Care – A UK and Ireland Network Survey Study

This study explores clinician understanding and perception at site lead level towards machine learning (ML) decision support tools for paediatric rela...

Machine Learning Fairness in Predicting Underweight, Overweight and Adiposity Across Socioeconomic and Caste Group in India: Evidence from the Longitudinal Ageing Study in India

Machine learning (ML) models are widely used to predict body mass index (BMI), yet their fairness across socioeconomic and caste groups remains uncert...

From Rule-Based to DeepSeek R1 – A Robust Comparative Evaluation of Fifty Years of Natural Language Processing (NLP) Models To Identify Inflammatory Bowel Disease Cohorts

Natural language processing (NLP) can identify cohorts of patients with inflammatory bowel disease (IBD) from free text. However, limited sharing of c...

Integrating Machine Learning for Propensity Score Matching and Causal Inference: A Causal Forest Approach to Assessing the Impact of Maternal Education on Antenatal Care Utilization

While maternal education is linked to antenatal care (ANC) use, its causal effect remains uncertain. This study applies a machine learning approach, C...

“Double Machine Learning for Causal Inference in High-Dimensional Electronic Health Records”

Estimating causal effects in observational health data is challenging due to confounding by indication. Traditional approaches such as inverse probabi...

Evaluating anti-LGBTQIA+ medical bias in large language models

Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While th...

Scaling Sensor Metadata Extraction for Exposure Health Using LLMs

The rapid evolution and diversity of sensor technologies, coupled with inconsistencies in how sensor metadata is reported across formats and sources, ...