Risk Management

Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data.

BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on ...

Randomised Controlled Multicentre Trial to Investigate the Effectiveness of a Mobile Artificial Intelligence Solution for Diabetes Adapted Care: The MELISSA Trial Protocol.

AIMS: Achieving optimal glycaemic control remains a burden for many people with diabetes on intensive insulin treatment. The MELISSA trial aims to clinically validate the artificial intelligence (AI)-based MELISSA system to support people with Type 1 diabetes on multiple daily insulin injections (MDI) with personalised insulin dose recommendations and an innovative approach for automatic carbohydr...

Artificial intelligence-assisted integrated care to promote colonoscopy uptake (AICC) in China: study protocol for a cluster randomized controlled trial.

BACKGROUND: Colorectal cancer (CRC) leads to heavy disease and economic burdens globally. Early screening such as colonoscopy has been demonstrated to...

Comparative evaluation of deep learning architectures for microbial colony classification in microbiological imaging.

BACKGROUND: Visual interpretation of microbial colonies on agar plates is an essential task for experienced microbiologists. In high-throughput workfl...

Evaluating the Accuracy, Quality, and Reproducibility of AI Chatbot-Generated Diabetes Self-Management Education and Support Plans Aligned With ADCES7.

BACKGROUND: The rapid development of artificial intelligence, particularly large language models (LLMs) such as ChatGPT, Gemini, and Claude, offers ne...

RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images.

Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work p...

Diagnostic accuracy of AI-assisted point-of-care ultrasound for abdominal free fluid detection in FAST trauma assessment: a systematic review and meta-analysis.

STUDY OBJECTIVE: Point-of-care ultrasound (PoCUS) is widely used in trauma care through the Focused Assessment with Sonography for Trauma (FAST) proto...

Toward protocol simplification: Deep learning-based image synthesis in three-phase CT urography.

OBJECTIVE: The purpose of this study was to develop and evaluate a method for synthesizing 3D urothelial phase images in CTU examinations from the dua...

Using large language models for automated assessment of reporting quality and completeness of prediction model studies.

BACKGROUND AND OBJECTIVES: Transparent and complete reporting in scientific papers is important for interpretation of study results and for downstream...

Machine learning to predict plasma-based CO2 conversion in dielectric barrier discharge reactors.

Plasma-based CO2 conversion is an emerging defossilization technology that converts a potent greenhouse gas into valuable chemical feedstocks, yet its...

Random Spatiotemporal Sampled-Data Control for Reaction-Diffusion Neural Networks With Dwell-Time-Based Sojourn Probability.

This article addresses the mean-square exponential synchronization problem of reaction-diffusion neural networks (RDNNs) subject to stochastic switchi...

Evaluating the Methodological Quality of Artificial Intelligence-Assisted Systematic Reviews: Protocol for a Mixed Methods Meta-Research Study.

BACKGROUND: Artificial intelligence (AI), including large language models (LLMs), is increasingly integrated into systematic review (SR) workflows. AI...

A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study.

BACKGROUND: Eligibility criteria are essential to clinical trial design, guiding recruitment, and ensuring patient safety and scientific rigor. Howeve...

Achieving Ultra-High Acceleration Rates in 7T MRI Using Combined Controlled Aliasing in Parallel Imaging and Compressed Sensing with Deep-Learning-Based Image Reconstruction.

BACKGROUND AND PURPOSE: Clinical adoption of 7T MRI has been limited by lengthy acquisitions. Acceleration techniques, such as controlled aliasing in ...

Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals' perspectives.

Explainable Artificial Intelligence (XAI) has the potential to enhance clinical decision support (CDS) systems however, it remains unclear how XAI sys...

Equitable Digital Frailty Screening for Marginalized Older Adults Using Audio Computer-Assisted Self-Interview: Collaborative Development Guide and User Testing Study.

BACKGROUND: Older adults facing social or structural marginalization for reasons such as lower literacy, digital exclusion, financial constraints, res...

Developing and validating an electronic health record-embedded AI model for managing multimorbid hospitalisation risk in patients with chronic RESpiratory disease (AiRES): a study protocol.

BACKGROUND: Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a ...

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).

The integration of artificial intelligence (AI) into clinical decision support (CDS) holds promise for proactive, personalized, and precision care. Ho...

Epistemic frontiers and the distinction between causality, information, and predictability in pattern recognition.

High predictive accuracy is frequently misinterpreted as evidence of causal understanding or population-level signal. Models can exploit spurious corr...

Machine learning model predicts acute kidney injury in pediatric patients after cardiac surgery: a systematic review and meta-analysis.

BACKGROUND: Acute kidney injury (AKI) is a common complication following pediatric cardiac surgery, frequently leading to poor outcomes and even death...