Emergency Medicine

Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.

Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neural data. Studies often focus on uniform single-site experiments in uniform settings, leading to high performance that may not translate well to real-w...

AI in radiology: From promise to practice - A guide to effective integration.

While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology, important obstacles still inhibit its integration into clinical environments. Foremost among them is the inability to integrate clinical information and prior and concurrent imaging examinations, which can lead to diagnostic errors that could irreversibly alter patient care. For AI to succeed in mo...

Predictive, integrative, and regulatory aspects of AI-driven computational toxicology - Highlights of the German Pharm-Tox Summit (GPTS) 2024.

The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DG...

Exploring the potential of artificial intelligence models for triage in the emergency department.

OBJECTIVE: To perform a comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use...

Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the emergency department.

INTRODUCTION: Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department ...

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review.

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Thr...

Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.

As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude...

Machine learning-assisted source tracing in domestic-industrial wastewater: A fluorescence information-based approach.

An emergency water pollution incident poses a significant risk to the proper functioning of wastewater treatment plants, particularly in domestic-indu...

Artificial intelligence and informatics in neonatal resuscitation.

Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be e...

Analysis of ChatGPT in the Triage of Common Spinal Complaints.

BACKGROUND: ChatGPT is a natural language processing chatbot with a significant prevalence in modern media with a clear application in the medical tri...

Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study.

Chest X-rays (CXRs) are crucial for diagnosing and managing lung conditions. While CXR is a common and cost-effective diagnostic tool, interpreting th...

[Possibilities of the utilization of trauma networks of the German Society for Trauma Surgery using digital solutions].

This paper describes the use of digital solutions to improve the care of trauma patients in Germany. The focus is on the trauma networks of the German...

Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.

Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over t...

Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.

Trustworthiness is crucial for artificial intelligence (AI) models in clinical settings, and a fundamental aspect of trustworthy AI is uncertainty qua...

Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.

PURPOSE: To assess the efficacy of radiomics features extracted from non-contrast computed tomography (NCCT) scans in differentiating multiple etiolog...

Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses.

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However...

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.

BACKGROUND: Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines propo...

Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.

: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of...

A large language model-based clinical decision support system for syncope recognition in the emergency department: A framework for clinical workflow integration.

Differentiation of syncope from transient loss of consciousness can be challenging in the emergency department (ED). Natural Language Processing (NLP)...

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it in...