Cultural Competence

Electronic Health Record-Based Prediction Models to Inform Decisions about HIV Pre-exposure Prophylaxis: A Systematic Review

Several clinical prediction models have been developed using electronic health records data to help inform decisions about HIV pre-exposure prophylaxis (PrEP) prescribing, but the characteristics and quality of these models have not been systematically assessed. We identified and critically appraised the characteristics and quality of studies reporting the development of electronic health records ...

SLaM Image Bank – a real-world diverse London cohort linking brain MRI to electronic mental health and dementia records for the development of clinical decision support tools using artificial intelligence

Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space have largely been limited to research cohorts with little real-world translation that is clinically meaningful for patients in psychiatry and neurology and those with associated neuropsychiatric symptoms. There is a lack of large real-world multimodal ...

Underdiagnosis Bias of Chest Radiograph Diagnostic AI can be Decomposed and Mitigated via Dataset Bias Attributions

Inequitable diagnostic accuracy is a broad concern in AI-based models. However, current characterizations of bias are narrow, and fail to account for ...

Benchmarking And Datasets For Ambient Clinical Documentation: A Scoping Review Of Existing Frameworks And Metrics For AI-Assisted Medical Note Generation

The increasing adoption of ambient artificial intelligence (AI) scribes in healthcare has created an urgent need for robust evaluation frameworks to a...

Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health Data

Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepre...

Quantifying Device Type and Handedness Biases in a Remote Parkinson’s Disease AI-Powered Assessment

Early detection of Parkinson’s Disease (PD) can enable early access to care, improving patient outcomes. We investigate the use of machine learning to...

Fair machine learning models for disease prediction: In-depth interviews with key health experts

Artificial intelligence (AI) and machine learning (ML) pose enormous potential for improving quality of life. It can also generate significant social,...

Assessing the Limitations of Large Language Models in Clinical Practice Guideline-concordant Treatment Decision-making on Real-world Data

Large Language Models (LLMs) have shown promise in therapeutic decision-making comparable to medical experts, but these studies have used highly curat...

Comparative Medical Ecology of Gut Microbiomes in Major Neurodegenerative, Neurodevelopmental, and Psychiatric (NNP) Disorders

This study provides a comprehensive medical ecology analysis of gut microbiome alterations in four neuropsychiatric disorders: Alzheimer’s disease (AD...

Is Multimodal Better? A Systematic Review of Multimodal versus Unimodal Machine Learning in Clinical Decision-Making

Machine learning has demonstrated success in clinical decision-making, yet the added value of multimodal approaches over unimodal models remains uncle...

Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions

Large language models’ (LLMs) alignment with ethical standards is unclear. We tested whether LLMs shift medical ethical decisions when given socio-dem...

AI-Driven and Automated Continuous Oxygen Saturation Monitoring and LTOT: A Systematic Review

Long-term oxygen therapy (LTOT) is essential for patients with chronic hypoxemia, particularly due to chronic obstructive pulmonary disease (COPD). Ho...

Deep Learning Analysis of Figure Copying Tasks for Parkinson’s Disease Detection with GAN-Based Data Augmentation

Early and accurate diagnosis of Parkinson’s disease (PD) is essential for enabling timely treatment and effective disease management. In this study, w...

Bridging AI and Healthcare: A Scoping Review of Retrieval-Augmented Generation—Ethics, Bias, Transparency, Improvements, and Applications

Retrieval-augmented generation (RAG) is an emerging artificial intelligence (AI) strategy that integrates encoded model knowledge with external data s...

Causal Forests versus Inverse Probability of Treatment Weighting to adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study based on US Hosptial Electronic Health Record Data

Rapid innovation and new regulations increase the need for post-marketing surveillance of implantable devices. However, complex multi-level confoundin...

AI-HOPE-TGFbeta: A Conversational AI Agent for Integrative Clinical and Genomic Analysis of TGF-β Pathway Alterations in Colorectal Cancer to Advance Precision Medicine

Early-onset colorectal cancer (EOCRC) is rising rapidly, particularly among Hispanic/Latino (H/L) populations, who face disproportionately poor outcom...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...

Systematic literature review of the use of generative AI in health economic evaluation

Generative AI (GenAI) has emerged in the current decade as a paradigm-shifting technology with potential to transform the process of health economic e...

Data Resource Profile: Linking electronic health and social records to study and lower health inequalities in cardiovascular diseases (BIG-HEART)

The BIG-HEART cohort was established to study and reduce health inequalities in cardiovascular disease by linking rich, multidimensional electronic he...

Short-term and long-term outcome prediction for patients with coronary artery disease using machine learning and comprehensive multi-center patient data

Revascularization decision-making for patients with coronary artery disease (CAD) can benefit from accurate patient outcome prediction. While previous...