Surveys

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 planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, I...

Synthetic Validation of Pediatric Trust Instruments using Persona-Driven Large Language Models

Trust is foundational to patient-physician relationships and is associated with improved care-seeking and adherence in primary care. However, validated trust instruments for pediatric emergency and surgical contexts are lacking, and traditional instrument development is slow and resource-intensive. Large language models (LLMs) could streamline the validation process by serving as scalable, systema...

Validation of Generative AI Techniques for Synthetic Data Generation in Multiple Sclerosis Research: A Comparison with Real-World Evidence from the Italian MS Registry

Large multiple sclerosis (MS) registries provide crucial real-world evidence but often suffer from missing data, inconsistencies, and privacy limitati...

Medical Clinical Minds Meet Artificial Intelligence: Italian Physicians’ Knowledge, Attitudes, and Concordance between Italian Physicians and AI-Generated Diagnoses. A National Cross-Sectional Study

Artificial Intelligence has increasingly been integrated into clinical practice, yet its adoption and perception among medical professionals remain po...

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...

Unintended Pregnancy and Preterm Birth in the United States: Causal Inference and Risk Prediction Using National Survey of Family Growth Data

Unintended pregnancy remains common in high income countries and has been linked to poorer maternal and neonatal outcomes. Whether pregnancy intention...

Prediction of Adolescent Internalizing Disorder Risk: Evidence from the Norwegian Mother, Father, and Child Cohort Study

Internalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early ident...

Develop and Validate A Fair Machine Learning Model to Indentify Patients with High Care-Continuity in Electronic Health Records Data

Electronic health record (EHR) data often missed care outside a given health system, resulting in data discontinuity. We aimed to: (1) quantify miscla...

FairCareNLP: An AI-Driven Patient Review Analyzer for Healthcare

To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to i...

Development and evaluation of a multivariate prediction model for diagnosing asthma in patients with clinically suspected asthma using capnography

Diagnosis of asthma in primary care is challenged by a multistep pathway with variable adherence leading to significant misdiagnosis, late diagnosis a...

Reliability of Artificial Intelligence-enhanced Electrocardiography

The scientific literature on artificial intelligence-enabled electrocardiography (AI-ECG) has defined a robust performance of AI models in detecting a...

Development of Alzheimer’s Disease Risk Score for Future Primary Care: A White-Box Approach

Interpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimer’s disease (AD) and promote...

Prognosis After First-Trimester Threatened Miscarriage: A Systematic Review, Prognostic Accuracy Meta-Analysis, And Prediction Modelling Review

Threatened miscarriage represents one of the most prevalent obstetric emergencies globally. Nevertheless, women experiencing first-trimester bleeding ...

Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

The principal objective served by this article is to identify key literature and provide an overview of the breadth of research in the field of machin...

When AI Meets the FDA: An Evaluation of Large Language Models Performance in Regulatory and Clinical Trial Data Extraction, Synthesis, and Analysis

Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed in...

A double-blind, crossover, non-inferiority randomized controlled trial where primary care providers and patients compare human- and AI-generated digital health messages: the AI-CARE study protocol

Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been sh...

Deceptive Bias Measurement in Deep Learning: Assessing Shortcut Reliance in TCGA Cancer Models

Bias in machine learning is a persistent challenge because it can create unfair outcomes, limit generalization, and reduce trust in real-world applica...

Machine Learning Analysis of Post-Acute COVID Symptoms Identifies Distinct Clusters, Severity Groups, and Trajectories

Questionnaires that capture patient-reported symptomatology provide low-cost but potentially high-value data for the de novo discovery of disease phen...

Build fair machine learning models to predict adverse outcomes for Heart failure patients with preserved ejection fraction (HFpEF) and with reduced ejection fraction (HFrEF)

Heart failure (HF), including heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), remains...