Cultural Competence

Assessment of Bias in Clinical Trials with LLMs Using ROBUST-RCT: A Feasibility Study

Bias assessment is a crucial step in evaluating evidence from randomized controlled trials. The widely adopted Cochrane RoB 2, designed to identify these issues, is complex, resource-intensive, and unreliable. Advances in artificial intelligence (AI), particularly in the field of large language models (LLMs), now allow the automation of complex tasks. While prior investigations have focused on whe...

Towards Participatory Precision Health: Systematic Review and Co-designed Guidelines For Adolescent Just-in-time Adaptive Interventions

Adolescence and young adulthood (10-25 years) constitute a sensitive developmental period marked by rapid biological, psychological, and social changes, during which preventative health interventions can shape long-term outcomes. Mobile health (mHealth) tools offer opportunities for tailored support but often with limited adaptation to adolescents’ dynamic contexts, resulting in inconsistent engag...

Automation Bias in Large Language Model Assisted Diagnostic Reasoning Among AI-Trained Physicians

Large language models (LLMs) show promise for improving clinical reasoning, but they also risk inducing automation bias, an over-reliance that can deg...

Factors influencing the trustworthiness of non-randomized studies of interventions: a survey of international experts

Perceived trustworthiness of research may be influenced by factors beyond the risk of bias, including study-related characteristics, research context,...

Retrospective Validation of an Artificial Intelligence System for Diagnostic Assessment of Prostate Biopsies on the ProMort Cohort: Study Protocol

Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason ...

Comparison of Two National Noise Models: Progress Towards an Integrated Noise Model for Environmental Health Research in the United States

Two sound level maps currently exist for the contiguous United States. One was developed by the National Park Service (NPS) using machine learning met...

Evaluating algorithm Fairness in Predicting Health Service Use and Unmet Need Across Socioeconomic and Caste Subgroups: Evidence from Longitudinal Ageing Study in India

Persistent socioeconomic and caste inequalities in India drive disparities in healthcare access. Machine learning (ML) models offer promise for foreca...

Privacy-Enhancing Sequential Learning under Heterogeneous Selection Bias in Multi-Site EHR Data

To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health ...

Oral Rinse Sourced Microbiota in Oral Health and Disease in a Representative U.S. Adult Population

This study reports genus-level oral rinse microbiota profiles in a population of 3,770 U.S. adults from NHANES 2009–2012. Oral conditions explained mo...

Correcting Algorithmic Bias in Machine Learning Prediction of Healthcare utilization in India

This study investigates how historical disparities in healthcare access influence machine learning (ML) predictions of healthcare utilization among ol...

Artificial Intelligence Models for Predicting Molecular Pathway Activity in Spinal Cord Injury: A Systematic Review

Spinal cord injury (SCI) remains a devastating neurological condition with high global incidence and minimal curative options. The pathobiology is mul...

Benchmarking Large Language Models and Clinicians Using Locally Generated Primary Healthcare Vignettes in Kenya

Large language models (LLMs) show promise on healthcare tasks, yet most evaluations emphasize multiple-choice accuracy rather than open-ended reasonin...

A Scoping Review of Algorithmic Equity, Data Diversity, and Inclusive Design in the Transformer Era of Clinical NLP

The rapid digitization of healthcare has positioned transformer-based natural language processing (NLP) models as powerful tools for managing clinical...

Pretraining Diversity and Clinical Metric Optimization Achieve State-of-the-Art Performance on ChestX-ray14

We achieved state-of-the-art performance on the NIH ChestX-ray14 multi-label classification task using a simple 3-model ensemble: mean ROC-AUC 0.940, ...

“Complex models, marginal benefits--a multi-centre development and validation study of early warning scores across 2·16 million patient admissions addressing intercurrent medical interventions”

The National Early Warning Score (NEWS) is a nationally recommended, clinically implemented system, used to prevent patient deterioration. While numer...

Recognizing “Conformity Bias” in Large Language Models: A New Risk for Clinical Use

The aim of the present study is to systematically investigate the phenomenon of Conformity Bias in contemporary LLMs, specifically evaluating how repe...

The Application of Artificial Intelligence in Healthcare Practice: An Umbrella Review

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment ...

Modelling Approaches for Predicting the Distribution of Skin NTDs: A Systematic Review

Skin neglected tropical diseases (NTDs) such as cutaneous leishmaniasis, lymphatic filariasis, mycetoma, and podoconiosis affect millions in endemic r...

Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms

Incomplete or incorrect causal theories are a key source of bias in machine learning (ML) algorithms. Community-engaged methodologies provide an avenu...

Sociodemographic Bias in Large Language Model Clinical Trial Screening

Large language models (LLMs) are increasingly used in randomized clinical trial (RCT) screening, but their potential for sociodemographic bias remains...