Medical Ethics / Professional Responsibility

M²B-Net: a lightweight multi-scale multi-attention boundary-aware network for liver tumor segmentation from CT images.

Liver tumor segmentation from CT images remains challenging due to large variations in lesion scale, blurred boundaries, low tissue contrast, and the high computational cost of existing deep learning models. This study aims to develop a lightweight yet accurate segmentation network suitable for clinical deployment. We propose a Multi-scale Multi-attention Boundary-aware Network (M²B-Net) based on ...

Legal Infoveillance of Unlicensed Medical Practices in South Korea Through Criminal Court Decisions Using Machine Learning: Retrospective Observational Study.

BACKGROUND: Unlicensed medical practices (UMPs) pose a substantial threat to patient safety and public health, but their clandestine nature makes them difficult to monitor through conventional surveillance systems. Legal epidemiology offers a framework for using judicial data to study hidden health-related misconduct, and machine learning (ML) may help convert unstructured legal texts into analyza...

Data After Death: Post-Mortem Ethics of Burn Patient Records and Images.

Burn care frequently relies on extensive documentation, including graphic photographic images and detailed clinical records. While these materials are...

AIDx: a locally deployable AI system for physician clinical decision support.

The dynamic environment of medicine, particularly in settings such as the Emergency Department, challenges physicians with an influx of patient data a...

Comparative performance of one-stage and two-stage deep learning models for instance segmentation of overhanging dental restorations on bitewing radiographs.

Accurate detection of overhanging dental restorations on bitewing radiographs is clinically important but remains challenging due to subtle marginal d...

Large language models and generative artificial intelligence in endodontics: a scoping review.

The aim of this study is to comprehensively examine the evolution of artificial intelligence (AI), specifically large language models (LLMs), in the f...

Data-Informed Intuition in Hepatology: Integrating Evidence, Context, and Clinical Reasoning.

Artificial intelligence and data-driven models are changing hepatology, but expert clinical judgment remains essential. Liver diseases are complex and...

Observational study of predictors and outcomes of lung cancer in never-smokers in the UK (OLIVE): study protocol.

INTRODUCTION: Lung cancer is commonly associated with smoking. However, if considered separately, lung cancer in never-smokers (LCINS) is the seventh ...

SBEM-UNet: A Semantic Boundary and Contour-Enhanced Framework for Semisupervised Medical Image Segmentation.

In medical image segmentation, inherent boundary ambiguity, tissue overlap, and weak intensity gradients often produce blurred or discontinuous edges,...

Explaining reported generative AI engagement in higher education: an extended TAM with ethical compatibility and reliance-based trust.

The rapid integration of generative artificial intelligence (AI) tools into higher education has intensified conversations regarding usefulness, ethic...

Accurate Determination of Atomic-Level Segregation at the Rare-Earth-Doped Al2O3 Grain Boundary.

Atomic structures of a Lu-segregated grain boundary (GB) in α-Al2O3 are identified using hybrid Monte Carlo and molecular dynamics (MCMD) simulations ...

A Coarse-to-Fine DoubleUNet Framework with Synergistic Loss for Accurate Fetal Head Circumference Measurement.

OBJECTIVE: Fetal head circumference (HC) measurement is a routine and indispensable examination during pregnancy, closely associated with fetal health...

Recommendations for disclosure of artificial intelligence in scientific writing and publishing: a regional anesthesia and pain medicine modified Delphi study.

INTRODUCTION: The use of artificial intelligence (AI) in the scientific process is advancing at a remarkable speed, thanks to continued innovations in...

Physics-informed neural network-based simulation of pulmonary arterial hemodynamics abnormalities.

OBJECTIVES: To predict abnormal pulmonary artery hemodynamics caused by ventricular septal defect (VSD) using Physics-Informed Neural Networks (PINN) ...

Biobanking: IoT, safety challenges and security prospects.

The biobank is a functional unit that facilitates and improves research by storing biological samples and associated data. As such, it is a key resour...

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

In the molecular design of high-performance materials, the physical properties (y) of chemical structures generated virtually by a computer is predict...

Knowledge-guided brain tumor segmentation via synchronized visual-semantic-topological prior fusion.

Brain tumor segmentation requires precise delineation of hierarchical structures from multi-sequence MRI. However, existing deep learning methods prim...

AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala.

Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies in...

Evaluation of super-resolution deep learning reconstruction on three-dimensional constructive interference in steady state for enhanced visualization of vestibular schwannomas.

We evaluated whether, compared with conventional deep learning reconstruction (DLR) and zero-filling interpolation (ZIP), super-resolution DLR (SR-DLR...

A reproducible data-driven parameter optimization framework for classical skull stripping methods across heterogeneous brain MRI datasets.

Accurate skull stripping is a critical preprocessing step for reliable brain MRI analysis, yet the performance of classical algorithms remains highly ...