Medical Ethics / Professional Responsibility

Stimulus reliability but not boundary distance manipulations violate the folded-X pattern of confidence.

The folded-X pattern has been identified as a critical signature of confidence: as conditions become easier, confidence increases for correct trials but decreases for error trials. However, recent work has identified violations of the folded-X pattern where easier conditions lead to increased confidence for both correct and error trials (double-increase pattern). Nevertheless, it remains unclear w...

CMT-Unet: leveraging stage-wise hybrid framework for enhanced accuracy and efficiency in medical image segmentation.

Achieving high-precision pixel-level segmentation in medical imaging necessitates both the preservation of fine-grained local details and the modeling of long-range contextual dependencies; nevertheless, convolutional, Transformer, and naive hybrid architectures struggle to adaptively balance these two requirements. To alleviate this limitation, we propose CMT-Unet, which incorporates a Mamba-base...

Enhanced medical image segmentation using optimized bidirectional LSTM and dolphin partner optimizer.

Medical imaging has become a standard in diagnosing and treating how organs and tissues operate. In earlier systems, machine learning approaches were ...

TADynFed: Dynamic modality-adaptive federated learning with tissue-aware disentanglement for cross-disease analysis.

Federated learning (FL) enables collaborative medical image analysis across decentralized institutions while preserving data privacy. However, real-wo...

In Situ Transmission Electron Microscopy Visualization of Electric-Field-Induced Phase Transitions at the Morphotropic Phase Boundary in Hf0.5Zr0.5O2.

Understanding electric-field-induced phase transitions is crucial for optimizing the ferroelectric and antiferroelectric properties of hafnium zirconi...

Automated Segmentation of Stellate Ganglion Block Region in Ultrasound Images Using Deep Learning Model.

BACKGROUND: The stellate ganglion region is densely vascularized and innervated, making the stellate ganglion block (SGB) technically challenging unde...

Implementing artificial intelligence (AI) to facilitate health outcomes in mood disorders: Application versus aspiration.

The burden of depressive and bipolar disorders at the individual and societal level are extraordinary and increasing. For decades, evidence-based trea...

Class-balanced dermoscopic lesion segmentation using MoG-LISA and optimized Swin-UNet via the GM-FDE framework.

Automatic skin lesion segmentation is one of the key pivotal tasks in dermatological image processing, with important consequences in early melanoma d...

Explainability-informed benchmarking of two deep learning models for organ-at-risk segmentation in MR-guided adaptive radiotherapy.

INTRODUCTION/BACKGROUND: Segmentation of gastrointestinal (GI) organs-at-risk (OARs) is a critical yet time-consuming step in MR-guided adaptive radio...

Enhancing dental clinical education through AI: insights into post-clinical summaries.

BACKGROUND: Artificial Intelligence (AI) is increasingly being introduced into clinical education, including dentistry, as a supplement to traditional...

Instant Answers, Enduring Responsibility: Teaching Judgment in the Age of Artificial Intelligence.

Artificial intelligence (AI) has become embedded in medical practice and education. In today's digital world, medical learners use AI tools to arrive ...

Transforming Grain-Boundary Brittle Precipitates to Ductility Pathways in Complex Concentrated Alloy.

Conventional wisdom holds that hard grain-boundary (GB) precipitates embrittle structural alloys by acting as crack initiation sites. In this work, we...

Boundary-aware and discrepancy-guided dynamic pseudo-labeling with consistency learning for semi-supervised 3D TOF-MRA cerebrovascular segmentation.

Objective.Cerebrovascular diseases are a major global health challenge due to their high morbidity and mortality rates. Accurate segmentation of cereb...

Intraoperative biopsy imaging of lung cancer risk.

BACKGROUND: Before surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy ...

Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting.

Artificial intelligence (AI) is increasingly used to support medical interpreting and public health communication, yet current systems introduce serio...

Linking Electronic Health Records for Multiple Sclerosis Research: Comparative Study of Deterministic, Probabilistic, and Machine Learning Linkage Methods.

BACKGROUND: Data linkage in pharmacoepidemiological research is commonly employed to ascertain exposures and outcomes or to obtain additional informat...

Loyalty-SMOTE: Data synthesis algorithm for effective imbalanced data classification.

Imbalanced datasets are always problematic in training machine learning models, so that classifiers often struggle to achieve satisfactory performance...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

A hybrid XGBoost-SVM ensemble framework for robust cyber-attack detection in the internet of medical things (IoMT).

Today, the rise of the Internet of Medical Things (IoMT) has evolved into a highly valued global market worth billions of dollars. However, this growt...

Exploring service user attitudes towards mental health technologies.

OBJECTIVES: Understanding service users' knowledge of and attitudes towards the rapidly progressing field of mental health technology (MHT) is an impo...