Medical Education

The impact of AI technology for ideological and political education teaching based on deep learning.

This study investigates the innovative use of deep learning models in ideological and political education (IPE) at vocational colleges. The study focuses on addressing two core challenges in traditional IPE: limited adaptability of educational resources and low student engagement. Using datasets related to resource allocation and learner performance, the study applies Graph Neural Networks (GNNs) ...

Simulation-based inference at the theoretical limit for fast, robust microstructural MRI with minimal diffusion data.

BACKGROUND: Diffusion-weighted magnetic resonance imaging provides a non-invasive way to probe brain tissue microstructure and is widely used in neuroscience and clinical research. Reliable microstructural maps usually require long scan times because many measurements are needed to sample the underlying parameter space. This limits clinical feasibility and accessibility. The aim of this study is t...

Predictability of Same-Day Discharge Candidacy After Bariatric Surgery with Machine Learning.

INTRODUCTION: Despite growing interest, same-day discharge (SDD) after bariatric surgery remains uncommon due to challenges with patient selection. In...

Evaluation of artificial intelligence generated immersive virtual reality simulation on skill mastery.

BACKGROUND: Nursing education faces challenges in providing nursing students with sufficient clinical site opportunities due to healthcare staffing sh...

Does the Timing of USMLE Step 1 Affect Performance? A Comparison of Pre- and Post-Clerkship Timing in the Pass/Fail Era.

In 2022, Step 1 of the United States Medical Licensing Examination transitioned to pass/fail scoring, removing a major performance-oriented incentive ...

XAI-Exit: Interpretability-Driven Dynamic Early Exits for Efficient and Transparent DNN Inference.

Deep neural networks (DNNs) excel across domains but face challenges in resource-constrained and critical settings due to high computational cost and ...

Beyond the hype: A simulation study evaluating the predictive performance of machine learning models in psychology.

Although machine learning (ML) methods are gaining popularity in psychological research, the debate about their usefulness ranges from hype to disillu...

General Intelligence-Based Fragmentation (GIF): A Framework for Peak-Labeled Spectra Simulation.

Despite growing reference libraries and advanced computational tools, progress in the field of metabolomics remains constrained by low rates of annota...

Artificial Intelligence-Powered Simulation for Telehealth Communication Training: Bridging Traditional Simulation and Artificial Intelligence.

INTRODUCTION: Traditional simulation-based communication training remains resource-intensive and difficult to scale. While artificial intelligence (AI...

Artificial intelligence related anxiety among dental students: associations with demographics and AI use behaviors.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into dental diagnostics and education, including AI-assisted radiograph interpreta...

Attention-enhanced spatiotemporal deep learning for predictive maintenance in oil and gas assets: towards Maintenance 5.0.

The adoption of Maintenance 5.0 signifies a shift towards an advanced level of human-centered asset management that prioritizes self-sufficiency, resi...

Source characterization of trace elements and impacts of salinization using geochemical modelling with machine learning approach and Monte-Carlo based health risk assessment in coastal aquifer zones.

This work investigated the trace element contamination, spatial distribution and their source of origin along with health risk assessment in the coast...

MolCrysKit: A Topology-Aware Toolkit for Bridging Experimental Molecular-Crystal Structures and Simulation-Ready Modeling.

Modeling molecular crystals requires addressing two main challenges: (i) maintaining the integrity of chemically bonded molecular units during structu...

"Do as I say and not as I do: Surgical critical care program directors shape the future" authors' response to the Letter to the Editor entitled: "Artificial intelligence literacy and infectious diseases competency: Essential considerations for future revisions of the surgical critical care curriculum".

Bridging the Implementation Gap in Medical AI Education: 3-Lever Framework for Concurrent Reform.

The rapid integration of artificial intelligence (AI) into clinical practice necessitates urgent restructuring of medical education and physician asse...

Educational strategies in prenatal anomaly scanning: teaching, simulation and competence assessment.

INTRODUCTION: Prenatal anomaly scanning is a core component of obstetric care, yet remains highly operator-dependent. Variability in training contribu...

Narrowing down the cause of the hard-sphere nucleation discrepancy: The free energy of precritical nuclei is consistent with predictions.

Predicting the rate of crystal nucleation is among the most substantial long-standing challenges in condensed matter. In the system most studied (hard...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

An Automated Curriculum to Support Behavioral Health Counseling Among Pediatric Residents: Usability Study.

BACKGROUND: Behavioral health concerns are common in pediatric practice, with pediatricians reporting a lack of skills related to providing effective ...

Modeling Diabetes Risk and Progression With Public Health Data: Ontology-Guided, Simulation-Capable Digital Twin Study.

BACKGROUND: Digital twins (DTs) offer a paradigm for health care by enabling data-driven, simulation-capable representations of individual health traj...