Primary Care

Machine learning models to detect opioid misuse in Emergency Department patients at triage

Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few receive such care. Universal manual screening has been proposed to improve patient identification but is uncommon due to its time and resource-intensive nature. We sought to determine the feasibility of identifying patients with opioid misuse at the t...

Beyond Accuracy: Multidimensional Evaluation of Large Language Models in Hepatocellular Carcinoma Management Emphasizing Prompting

Hepatocellular carcinoma is the most common type of primary liver cancer and remains a major global health challenge. In resource-limited settings, patients often face barriers such as low screening rates, poor adherence, and limited access to medical information. Despite comprehensive clinical guidelines, issues like inadequate patient education and ineffective communication persist. While large ...

MedAdhereAI: An Interpretable Machine Learning Pipeline for Predicting Medication Non-Adherence in Chronic Disease Patients Using Real-World Refill Data

Medication non-adherence remains a significant challenge in managing chronic conditions like diabetes and hypertension, leading to increased morbidity...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

The impacts of artificial intelligence on the workload of diagnostic radiology services: A rapid review and stakeholder contextualisation

Advancements in imaging technology, alongside increasing longevity and co-morbidities, have led to heightened demand for diagnostic radiology services...

Validation of Synthesa AI, a Large Language Model-Based Screening Tool for Systematic Reviews: Results from Nine Studies

Systematic review screening is often burdensome, prone to human error, and requires significant manual effort. Synthesa AI, a large language model (LL...

Characteristics and Early Diagnosis of Motor Neuron Disease (MND) in 67 million individuals in England: a comparative study on phenotyping models derived by AI, Knowledge Graphs and the MND Association

Motor neuron disease (MND) is a rapidly progressive and fatal neurodegenerative condition, making early diagnosis critical for optimizing patient outc...

A conversational artificial intelligence based web application for medical conversations: a prototype for a chatbot

Artificial Intelligence (AI) has evolved through various trends, with different subfields gaining prominence over time. Currently, Conversational Arti...

High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort

Synthetic data generated using generative models trained on real clinical data offers a promising solution to privacy concerns in health research. How...

Evaluating the accuracy and consistency of ChatGPT for the management of type 2 diabetes: A cross-sectional study

Large language models (LLMs) have fundamentally changed how patients and clinicians retrieve information; however, it is unclear how accurate and cons...

Multiethnic Validation of Artificial Intelligence-Enhanced Electrocardiographic Image Analysis in Detecting Cardiac Structural and Functional Abnormalities: A UK Biobank Study

Although artificial intelligence–enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, large-scale validation ag...

Deep learning predicts cardiac output from seismocardiographic signals in heart failure

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks...

A Systematic Review of the Application of Computational Grounded Theory Method in Healthcare Research

The integration of computational methods with traditional qualitative research approaches has emerged as a transformative paradigm in healthcare resea...

Machine Learning-Based Pattern Recognition of Risk Factors for Low Back Pain among Adolescent Cricket Players in Dhaka City

Low back pain (LBP) is common among adolescent cricketers, often due to repetitive lumbar stress. This study investigated LBP among 450 adolescent cri...

Leveraging Large Language Models and Patient Portal Messages for Early Identification of Depression

Large language model (LLM)-assisted early warning system may help overcome existing barriers to timely depression diagnosis in patients with cardiovas...

Artificial Intelligence-based Automated Echocardiographic Analysis and the Workflow of Sonographers: A randomized crossover trial

This randomized crossover trial aimed to evaluate whether an artificial intelligence (AI)-based automatic analysis tool for echocardiography could imp...

Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study

Approximately 70% of adults with chronic obstructive pulmonary disease (COPD) remain undiagnosed. Opportunistic screening using chest computed tomogra...

Weakly Supervised Active Learning for Abstract Screening Leveraging LLM-Based Pseudo-Labeling

Abstract screening is a notoriously labour-intensive step in systematic reviews. AI-aided abstract screening faces several grand challenges, such as t...

Performance of Universal and Stratified Computer-Aided Detection Thresholds for Chest X-Ray-Based Tuberculosis Screening: A Cross-Sectional Diagnostic Accuracy Study

Computer-aided detection (CAD) software analyzes chest X-rays for features suggestive of tuberculosis (TB) and provides a numeric abnormality score. H...

Using discrete- and continuous-time machine learning models (Nnet, CoxNet, GLMnet) to explore sex and age differences in stroke prediction among hypertensive individuals

Stroke is one of the leading causes of death and long-term disability globally. Several studies have investigated the incidence and predictors of stro...