Clinical Trials

The role of artificial intelligence in the application of the integrated electronic health records and patient-generated health data

This scoping review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in health care, including clinical decision support, health care quality, and patient safety. We focused on the integrated data that combined PGHD and EHR data, and we investigated the role of artificial i...

Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents

Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear and ensemble machine learning (ML) models and applies explainable AI (XAI) techniques to understand the contribution of input features. Data from AgInjuryNews (2015–2024) was preprocessed to...

A Framework to Assess Clinical Safety and Hallucination Rates of LLMs for Medical Text Summarisation

Integrating large language models (LLMs) into healthcare settings can improve workflow efficiency and patient care by automating tasks such as summari...

NutriRAG: Unleashing the Power of Large Language Models for Food Identification and Classification through Retrieval Methods

This study explores the use of advanced Natural Language Processing (NLP) techniques to enhance food classification and dietary analysis using raw tex...

Extracting Pulmonary Embolism Diagnoses from Radiology Impressions Using GPT-4o: A Large Language Model Evaluation Study

Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually...

irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets

Large language models (LLMs) have emerged as transformative technologies, revolutionizing natural language understanding and generation across various...

Evaluating biomedical feature fusion on machine learning’s predictability and interpretability of COVID-19 severity types

Accurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow. Current techniqu...

Transport-based transfer learning on Electronic Health Records: Application to detection of treatment disparities

Electronic Health Records (EHRs) sampled from different populations can introduce unwanted bi-ases, limit individual-level data sharing, and make the ...

Exploring the Potential of Large Language Models for Automated Safety Plan Scoring in Outpatient Mental Health Settings

The Safety Planning Intervention (SPI) produces a plan to help manage patients’ suicide risk. High-quality safety plans – that is, those with greater ...

A Deep Learning Framework for Causal Inference in Clinical Trial Design: The CURE AI Large Clinicogenomic Foundation Model

Clinical research is limited by the capability to define the most important combinations of clinical features and biomarkers that predict therapeutic ...

Evaluating the Reporting Quality of 21,041 Randomized Controlled Trial Articles

Incomplete reporting of a study’s methods and results hinders efforts to evaluate and reproduce research findings in randomized controlled trials (RCT...

Real-World Evaluation of Large Language Models in Healthcare (RWE-LLM): A New Realm of AI Safety & Validation

The deployment of artificial intelligence (AI) in healthcare necessitates robust safety validation frameworks, particularly for systems directly inter...

Emulating Clinical Trials with the Mayo Clinic Platform: Cardiovascular Research Perspective

Randomized controlled trials (RCTs) provide the highest level of clinical evidence but are often limited by cost, time, and ethical constraints. Emula...

Screening for anemia using multi-modal machine learning models on smartphones: protocol for a comparative accuracy study in rural India

Anemia, or low blood hemoglobin (Hb), affects one third of the world population, and is particularly prevalent in women and children in lower resource...

Limited Echocardiogram Acquisition by Clinicians Aided with Deep Learning: A Randomized Controlled Trial

Deep learning (DL) programs can aid in the acquisition of echocardiograms by medical professionals not previously trained in sonography, potentially a...

Extracting TNFi Switching Reasons and Trajectories From Real-World Data Using Large Language Models

Tumor necrosis factor inhibitors (TNFi) are widely used for auto-immune conditions. Despite their efficacy, many patients switch TNFis due to lack of ...

PED-X-Bench: A Benchmark of Adult-to-Pediatric Extrapolation Decisions in FDA Drug Labels

Pediatric trials are ethically and logistically difficult, so the U.S. FDA often extrapolates adult data to children when justified. Yet no public res...

Real-world Validation of MedSearch: a conversational agent for real-time, evidence-based medical question-answering

Application of Large Language Models (LLMs) powered Conversation Agents (CAs) in healthcare has been evaluated using medical question-answering (QA) d...

Optimizing the Clinical Application of Rheumatology Guidelines Using Large Language Models: A Retrieval-Augmented Generation Framework Integrating EULAR and ACR Recommendations

Timely access to current rheumatology guidelines at the point of care is challenging. We aimed to develop and evaluate the first Retrieval-Augmented G...

Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit

Genomic testing has transformed treatment decisions for hormone receptor-positive, HER2-negative (HR+/HER2-) early breast cancer; however, it remains ...