Medical Ethics / Professional Responsibility

Automated grain analysis via data augmentation and grain boundary detection.

This study presents an AI-enhanced framework to address key challenges in the quantitative metallographic analysis of pure iron systems. Manual grain size characterization suffers from limited efficiency and reproducibility, while existing computational methods are constrained by scarce data and incomplete grain boundary detection. To overcome these issues, we propose three core innovations. First...

Optimizing boundary dynamics for nested named entity recognition via semantic refinement and trimming.

Nested Named Entity Recognition (Nested NER) addresses the complex task of identifying and classifying entity spans embedded within other entities in textual data. Despite advances, existing span-based methods primarily rely on exhaustive span enumeration without adequately accounting for subtle semantic differences at entity boundaries, leading to boundary ambiguity and inaccurate entity delineat...

18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients.

OBJECTIVE: The aim of this study was to investigate the diagnostic performance of the 2.5-dimensional (2.5D) ensemble deep learning (DL) model based o...

Internal-external boundary attention fusion for glass surface segmentation.

Detecting transparent objects and mirrors in an image is a highly challenging task because their glass surfaces contain the visual appearance of other...

Incremental transfer learning based on temporal-frequency convolution interaction for multi-task prediction of wind speed and wind power.

In the domain of wind energy, predicting wind speed and power is a challenging and important task, yet they are closely intertwined. However, the temp...

Deep Learning for Automatic Segmentation of Pituitary Adenomas: A Videomics Study.

BACKGROUND AND OBJECTIVES: Videomics, which integrates video-endoscopy and artificial intelligence, presents significant potential for real-time surgi...

Fine-tuning large language models in federated learning with fairness-aware prompt selection.

Large language models (LLMs) require domain-specific fine-tuning for real-world deployment, yet face critical barriers of data privacy and computation...

Generating synthetic patient vignettes from real medical texts for the teaching of clinical reasoning.

WHAT WAS THE EDUCATIONAL CHALLENGE?: Experience with simulated clinical cases is a relevant component in the development of clinical reasoning (CR). G...

Generative AI in perioperative medicine and anesthesiology: ethical integration, educational innovation, and the future of clinical professionalism.

Generative artificial intelligence (AI) is rapidly transforming perioperative medicine, particularly anesthesiology, by enabling novel applications, s...

Large Language Models Use in Dry Eye Disease: Perplexity AI versus ChatGPT4.

PURPOSE: To compare the utility of two large language models (LLM) in dry eye disease (DED) clinics and research. METHODS: Trained ocular surface expe...

Evaluation of deep learning models for anterior segment OCT image segmentation during scleral lens wear.

PURPOSE: The accurate segmentation of corneal and contact lens boundaries in anterior segment optical coherence tomography (AS-OCT) images provides es...

Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.

This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP)...

A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.

Medical imaging has become an essential tool in the diagnosis and treatment of various diseases, and provides critical insights through ultrasound, MR...

AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols.

Accurate segmentation of brain tumors from multimodal Magnetic Resonance Imaging (MRI) plays a critical role in diagnosis, treatment planning, and dis...

Asynchronous Boundary Stabilization of Stochastic Markovian Reaction-Diffusion Neural Networks With Mode-Dependent Delays.

This article tackles asynchronous control issue for a class of stochastic Markovian reaction-diffusion neural networks with mode-dependent delays (MDD...

Health consumers' use and perceptions of health information from generative artificial intelligence chatbots: A scoping review.

Background Health consumers can use generative artificial intelligence (GenAI) chatbots to seek health information. As GenAI chatbots continue to impr...

Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.

Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healt...

Multi-scale fusion semantic enhancement network for medical image segmentation.

The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatmen...

Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curativ...

Enhanced abdominal multi-organ segmentation with 3D UNet and UNet +  + deep neural networks utilizing the MONAI framework.

Accurate segmentation of organs in the abdomen is a primary requirement for any medical analysis and treatment planning. In this study, we propose an ...