Ethics

Ultrafast Infant Brain Quantitative MRI Using Overlapping-Echo Acquisition with Volumetric Physical Simulation of Slice-level Non-Idealities.

OBJECTIVE: Quantitative MRI (qMRI) is sensitive to brain microstructural and metabolic changes; however, existing techniques often unsuitable for assessing postnatal brain development due to prolonged scan time, non-ideal imaging conditions, and severe infant motion. In this study, we propose a robust qMRI framework tailored for infant brain imaging to address abovementioned limitations. METHODS: ...

Non-European migrants with schizophrenia spectrum disorders in Swiss forensic and general psychiatric care facilities - A comparative study using machine learning.

Personal history of migration poses an important risk factor for schizophrenia spectrum disorders (SSD), which are also associated with a higher rate of criminal behavior. To enhance care for migrants, a vulnerable and often stigmatized group in both general and forensic psychiatry, this study investigates clinical, therapeutic, and psychopathological differences between non-European migrants diag...

Beyond saponins: An integrated mass spectrometry strategy for profiling, spatial mapping, and rapid authentication of non-saponin constituents in Panax species.

Non-saponin constituents of Panax species, including amino acids, sugars, and nucleosides, have attracted increasing attention due to their nutritiona...

Digital bioethics: exploring an emerging field.

The uptake of social science methods by bioethics significantly expanded its methodological spectrum, raising new theoretical, methodological, and pra...

Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial.

INTRODUCTION: Liver tumours are a leading cause of global morbidity and mortality. Current diagnostic tools, including computed tomography (CT), magne...

Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant.

Filamentous bulking (FB) is a recurring cause of solids-separation failure in activated sludge systems. Standard diagnostics such as sludge volume ind...

Artificial Intelligence in Human Participant Research: Ethical Risks and Institutional Policy Considerations.

The rapid integration of artificial intelligence (AI) into research presents emerging ethical and governance challenges for institutions overseeing hu...

HP-DIL: Deep heterogeneity profiling with graph-informed disentangled interaction learning for MRI-based liver fibrosis staging.

Liver fibrosis staging (LFS) informs treatment decisions and prognostic assessment in liver disease. Multiparametric MRI enables non-invasive, quantit...

AI-Generated Clinical Images of Head-and-Neck Lesions in Plastic Surgery Education: An Expert Evaluation.

BACKGROUND: Clinical images are essential in plastic and reconstructive surgery education, particularly for understanding pathology, planning reconstr...

Bayesian optimization for uncertainty-aware prediction of rainfall-induced deformation in embankment dams.

Reliable early warning of embankment dam failure requires predictive models that are accurate, physically consistent, and uncertainty-calibrated. This...

Integrating Gait Analysis and AI Into Knee Osteoarthritis Care: Protocol for a 3-Phase Participatory Qualitative Study.

BACKGROUND: Knee osteoarthritis (OA) leads to pain, disability, and reduced quality of life. For advanced stages, knee arthroplasty surgery is the sta...

Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines.

BACKGROUND: In the era of artificial intelligence (AI), nursing science has the potential to enable transformative change in healthcare driven by the ...

Privacy-preserving multimodal federated learning pipeline for cyber-resilient healthcare systems.

The integration of Internet of Things (IoT) devices and electronic medical records (EMRs) has transformed healthcare delivery but has also created new...

DIA-PINN. A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data.

Background: Pressure-volume (PV) loop analysis remains the gold standard for assessing the intrinsic global diastolic properties of the left ventricle...

Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with and without obstructive coronary artery disease: a study protocol.

INTRODUCTION: Angina with no obstructive coronary artery disease (ANOCA) affects millions and is frequently under-recognised because diagnostic pathwa...

Advancing neurotech justice in youth digital mental health: insights from an interdisciplinary and cross-generational workshop.

Researchers and clinicians are increasingly looking to leverage artificial intelligence (AI) and digital tools to improve psychiatric care. Of particu...

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

INTRODUCTION: Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care provide...

Artificial intelligence in scholarly peer review: a scoping review of applications, risks, and governance challenges.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into scholarly publishing workflows, extending beyond manuscript preparation into ...

A responsible AI framework for infection surveillance in low-resource settings: ethics, opportunities and threats for LMICs (EOT-LMICs).

The integration of artificial intelligence (AI) into infection surveillance represents a significant shift in public health; however, most AI framewor...

An ethics-informed computable audit framework for monitoring misdiagnosis risk in AI-assisted diagnosis.

Diagnostic AI can misclassify under distribution shift and subgroup imbalance; governance signals are rarely computable at deploy time. We target depl...