Latest Articles for Healthcare Technology

GNAN combines interpretability and accuracy for graph-based AI models.

This article discusses role-playing and personalization in language models.

This study explores advanced methods for efficient data labeling using neural network techniques.

Introducing CUT, a framework for realistic and diverse anomaly generation without extra training.

This study assesses how aiChat compares to medical librarians in answering clinical questions.

A new method enhances uncertainty estimation in machine learning predictions.

Cardiovascular digital twins help tailor treatments by analyzing individual patient data.

A new framework enhances prediction accuracy and data privacy in federated learning.

A framework to measure and improve uncertainty in image generation models.

HOGE enhances explanations of Graph Neural Networks using cell complexes.

CVQBM offers novel methods for data generation and analysis using quantum technology.

Research explores new methods for breast imaging without traditional contrast agents.

Advances in automatic lymph node segmentation enhance cancer treatment accuracy.

Mamba models improve accuracy and efficiency in interpreting medical images.

WGBoost offers advanced probabilistic predictions and better uncertainty handling in various fields.

An advanced method for frequency estimation while safeguarding data privacy.

A new framework aims to improve accuracy and efficiency in medical image analysis.

Research explores advanced loss functions for improving GAN performance using Genetic Programming.

A method to assess the reliability of AI outputs in healthcare and beyond.

Explore how AI models can improve healthcare data and patient outcomes.

Machine learning enhances ECG monitoring, improving patient care and heart health analysis.

A new approach enhances model reliability in crucial predictions.

New framework enables efficient learning of diseases without storing past data.

Understanding deep learning models improves trust in medical imaging diagnoses.

ICU-Sepsis aids researchers in testing AI algorithms for better sepsis management.

New methods improve CT imaging for better medical diagnosis.

DeltaNet improves efficiency in processing and recalling information for various applications.

A new approach enhances accuracy in localization systems by tackling sensor perspective shifts.

Exploring how AI can improve teamwork by understanding human beliefs.

Introducing MAP, an efficient way to merge multiple machine learning models.

Study examines the robustness of segmentation models against adversarial attacks in healthcare.

New framework uses 3D images for precise radiology reports.

Addressing missing data is vital for reliable healthcare research and patient care.

A novel method enhances personalized learning for large language models.

Combining AI methods to improve patient observation and care in healthcare.

Exploring privacy threats in image processing using diffusion models and leaked gradients.

Researchers aim to improve machine understanding of daily activities through video analysis.

Robots learn to adapt through vision and imitation techniques, improving usability.

This study presents an innovative method for generating trustworthy explanations in automated medical coding.

A new method improves model transparency and trust in critical areas like healthcare.