Emergency Medicine

Artificial Intelligence in Personalized Breast Cancer Drug Safety: From Preclinical Toxicology to Clinical Risk Management.

PURPOSE: The artificial intelligence (AI) implementation in personalized medicine has transformed drug safety, especially in breast cancer treatment. The importance of the need to treat breast cancer individually is acute as the disorder is heterogeneous and reacts differently to the use of chemotherapeutic agents. METHODS: Use of AI technologies including machine learning algorithms, deep learnin...

Network toxicology and single-cell transcriptomics nominate candidate pyrethroid-associated targets and pathways in clear cell renal cell carcinoma.

Pyrethroid insecticides are widely used in agricultural and domestic settings. Increasing evidence suggests that pyrethroid exposure may harm multiple organ systems and is associated with potential carcinogenicity. However, the mechanisms linking pyrethroids to clear cell renal cell carcinoma (ccRCC) remain unclear. Using an integrative framework combining network toxicology, single-cell sequencin...

Letter to the Editor: Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients.

Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study.

BACKGROUND: Current prompting techniques for large language models (LLMs), such as ChatGPT, mainly focus on well-structured, low-uncertainty problems;...

Neural Network Machine Learning for Determining Surgical Appropriateness in Head and Neck Subspecialty Referrals.

OBJECTIVE: Timely, accurate referrals to head and neck cancer surgery are essential for survival but are often delayed or misrouted, contributing to l...

AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings.

BACKGROUND Artificial intelligence (AI) is increasingly explored as a clinical decision-support tool in nephrology; however, its real-world applicabil...

Carotid Imaging: Current Concepts and Advanced Imaging.

Carotid atherosclerosis is a major cause of ischemic stroke, historically managed according to luminal stenosis severity. However, stenosis alone fail...

Publicly available multimodal large language models for ocular surface infections: benchmarking against corneal specialists in triage, diagnosis and treatment.

BACKGROUND/AIMS: Ocular surface infections remain a major cause of visual loss worldwide, yet diagnosis often relies on slow or insensitive microbiolo...

Artificial intelligence in scholarly peer review: a scoping review of applications, risks, and governance challenges.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into scholarly publishing workflows, extending beyond manuscript preparation into ...

The Role of Sulfur in Single-Walled Carbon Nanotube Growth.

It was broadly known that the addition of a small amount of sulfur into the reactor drastically changes the behavior of single-walled carbon nanotube ...

Integrating Machine Learning into Fracture Liaison Services: Toward precision risk stratification in osteoporosis care: A prospective cohort study.

BACKGROUND: Traditional fracture risk assessment tools have limitations in accurately predicting re-fracture risk. Machine learning (ML) approaches of...

Predicting public Intensive care unit mortality and hospitalization using Data: An evaluation of Brazil's Largest COVID-19 epidemiological dataset.

OBJECTIVES: To assess the SRAG dataset's potential for modeling COVID-19 mortality and LOS-ICU, identify key data gaps, and support the development of...

Three decades of hemodynamic monitoring (1995-2025): from invasive pressure based hemodynamic profiling to functional physiology.

PURPOSE OF REVIEW: Hemodynamic monitoring has undergone a profound transformation over the last 30 years. The field has transitioned from the "standar...

AI Models for Surgical Decision Support in Spontaneous Intracerebral Hemorrhage: A Systematic Review in Relation to Trials and Guidelines.

Artificial intelligence (AI) applications for spontaneous intracerebral hemorrhage (ICH) are rapidly expanding, particularly in perioperative imaging ...

Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnos...

An effluent risk informed closed-loop framework for early warning of influent anomalies using COD soft sensing.

Sudden shock loads in wastewater influent can severely disrupt biological treatment processes and cause effluent quality exceedances in wastewater tre...

Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study.

Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health...

Developing an artificial intelligence tool for detecting fractures of child abuse: preliminary findings.

OBJECTIVES: Approximately 6.9% of children in the United Kingdom have suffered physical abuse. Fractures are a common sign and must not be overlooked ...

A hierarchical prompt and prototype learning framework for brain disorder classification.

Accurate diagnosis of brain disorders (BDs) is challenging in clinical practice. Most existing deep learning-based methods perform diagnosis only in a...

From predictive accuracy to clinical utility: Model interpretability and the missing bone mineral density data.

This letter to the editor commends the study by Liu et al. on their machine learning model for predicting rib fractures but highlights two crucial cha...