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Prevention of medical errors

Latest AI and machine learning research in prevention of medical errors for healthcare professionals.

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QuantumXCT: Learning Interaction-Induced State Transformation in Cell-Cell Communication via Quantum Entanglement and Generative Modeling

Inferring cell-cell communication (CCC) from single-cell transcriptomics remains fundamentally limit...

PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling

In recent years, progress in medical informatics and machine learning has been accelerated by the av...

Mapping spatial cell-cell communication programs by tailoring chains of cells for transformer neural networks

Recent advances in spatial transcriptomics and computational modeling enable the study of cellular i...

IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning

Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Arch...

FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation

Federated learning (FL) offers a privacy-preserving paradigm for collaborative medical image analysi...

UAV-MARL: Multi-Agent Reinforcement Learning for Time-Critical and Dynamic Medical Supply Delivery

Unmanned aerial vehicles (UAVs) are increasingly used to support time-critical medical supply delive...

Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI

Foundation models (FMs) show great promise for robust downstream performance across medical imaging ...

Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentation

Medical image segmentation is fundamental to clinical workflows, yet models trained on a single data...

AI-Generated Responses to Patient's Messages: Effectiveness, Feasibility and Implementation

Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden...

Leveraging large language models to address common vaccination myths and misconceptions

Large language models (LLMs) are increasingly used by the public to seek health information, yet the...

Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices

Internet of Things (IoT) networks face significant challenges such as limited communication bandwidt...

Patient-centric radiology: Utilising large language models (LLMs) to improve patient communication and education

Purpose: To evaluate whether large language models (LLMs) can enhance clinician-patient communicatio...

GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task

Federated learning (FL) facilitates the secure utilization of decentralized images, advancing applic...

Communication-Inspired Tokenization for Structured Image Representations

Discrete image tokenizers have emerged as a key component of modern vision and multimodal systems, p...

Disparities, Perceived Discrimination, and Patient-Clinician Communication in Alcohol Use Disorder Treatment: An All of Us Cohort Study

Background and Aims: Alcohol use disorder (AUD) remains a major public health concern, with persiste...

On the Rate-Distortion-Complexity Tradeoff for Semantic Communication

Semantic communication is a novel communication paradigm that focuses on conveying the user's intend...

Data-Driven Multimodal Subtyping Reveals Differential Cognitive Risk and Treatment Effects in the All of Us Cohort

INTRODUCTION: Cognitively unimpaired (CU) adults show substantial variation in their risk of develop...

FastUSP: A Multi-Level Collaborative Acceleration Framework for Distributed Diffusion Model Inference

Large-scale diffusion models such as FLUX (12B parameters) and Stable Diffusion 3 (8B parameters) re...

ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning

Scaling federated learning (FL) to billion-parameter models introduces critical trade-offs between c...

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