Risk Management

Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.

BACKGROUND: Despite the increasing number of studies on prediction models for identifying the risk of postpartum post-traumatic stress disorder (PP-PTSD), the quality and clinical applicability of these models have not been clarified yet. OBJECTIVES: To systematically review and appraise the prediction models for PP-PTSD. METHODS: From inception to March 30, 2026, Web of Science, PubMed, the Cochr...

Machine learning prediction of PICC-associated thrombotic complications in critically ill patients.

BACKGROUND: Peripherally inserted central catheters (PICCs) are widely used vascular access devices in intensive care, yet thrombotic complications remain a significant clinical concern. Traditional risk assessment tools fail to capture the complex, non-linear interactions among thrombosis risk factors in critically ill patients. METHODS: This retrospective cohort study, conducted in adherence to ...

Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care.

BACKGROUND: Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifia...

Autism spectrum disorder identification using machine learning models on MRI data.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects sensory processing, speech, behavior and identifying the condition at an...

Clinical governance of artificial intelligence in internal medicine: a literature-informed five-pillar framework for complex care.

Most artificial intelligence (AI) governance frameworks in healthcare address model development, reporting standards, or regulation in broad terms, bu...

The impact of an artificial intelligence (AI)-assisted education program on mental health, social support and quality of life in older individuals with head and neck cancer: study protocol of a randomized controlled trial.

BACKGROUND: Head and neck cancer (HNC) is a common malignant tumor, and its treatment often leads to functional impairments in speech, swallowing, and...

Machine learning-driven multi-objective optimization of Gyroid bioglass scaffolds for site-specific biomechanical adaptation.

The clinical translation of scaffold-driven bone regeneration is hindered by an inherent topological paradox between mechanical integrity and mass tra...

Improving healthcare professionals' usage of artificial intelligence-powered risk recommenders through enhancement of trust and acceptance: a rapid realist review protocol.

INTRODUCTION: Healthcare professionals working in busy hospital environments are expected to make multiple back-to-back critical decisions related to ...

Physics-informed machine learning for cross-study prediction of oil-water membrane fouling.

Performance decay driven by coupled transport, accumulation, and removal processes remains a central challenge in many chemical engineering systems. M...

Current trends in machine learning for surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) is being transformed by the widespread adoption of artificial intelligence across the full methodological s...

Automated identification of MRI series using a hierarchical modular machine-learning pipeline.

OBJECTIVE: The volume and diversity of large MR imaging datasets require efficient automated labelling tools for cataloguing MR series, as manual anno...

Clinical evaluation of accelerated breath-held and free-breathing cine cardiac MRI using model-based deep learning reconstruction (SonicDL) in children and young adults.

BACKGROUND: Balanced steady-state free-precession (bSSFP) cine imaging is the clinical standard for ventricular function assessment but requires multi...

[Legal responsibility in AI-assisted radiological reporting].

BACKGROUND: Artificial intelligence (AI) is increasingly used in radiological diagnostics, particularly for screening, detection, and prioritization o...

Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.

OBJECTIVE: Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-...

Continued excellence in surgical care: How to integrate artificial intelligence systems while mitigating liability.

Arrhythmia Burden and Clinical Responses Under Continuous Monitoring in Heart Failure: Observations From the ALLEVIATE-HF Trial.

BACKGROUND: Arrhythmia burden in ambulatory patients with symptomatic heart failure (HF) without cardiac implantable electronic devices (CIEDs) is not...

An AI-Enabled Predictive Harm Response Management Algorithmic Tool to Reduce Adverse Events in Health Care Settings (PreHaRM): Protocol for a Three-Phase Model Development and Pilot-Testing Study.

BACKGROUND: Current data analysis and coordination methods do not effectively support nurses and midwives in risk reduction, as retrospective reportin...

Reducing Mis-triage in Emergency Departments (RemEDy): Protocol for Improving Triage Accuracy Through Real-time Evaluation and Artificial Intelligence.

BACKGROUND: Mis-triage represents a global concern, with reported rates ranging from 15% to 33%. Understanding its causes and contributing factors is ...

Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis.

BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress di...

Voice-assisted artificial intelligence in cardiovascular disease management: a systematic review and meta-analysis protocol.

INTRODUCTION: Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality, with self-management playing a pivotal role in i...