Latest AI and machine learning research in emergency medicine for healthcare professionals.
Can small language models with 0.5B to 5B parameters meaningfully engage in trauma-informed, empathetic dialogue for individuals with PTSD? We address this question by introducing TIDE, a dataset of 10,000 two-turn dialogues spanning 500 diverse PTSD client personas and grounded in a three-factor empathy model: emotion recognition, distress normalization, and supportive reflection. All scenarios...
Early prevention and standardized management of refractory wounds in the elderly are very important for improving prognosis, reducing disability rate, and improving the quality of life of patients. For the diagnosis and treatment of refractory wounds in the elderly, it is necessary to comprehensively consider the primary disease or comorbidity of patients and systematically evaluate the overall co...
Centrilobular hepatocyte hypertrophy is frequently observed in animal studies for chemical safety assessment. Although its toxicological significance ...
IMPORTANCE: Norepinephrine is the first-line vasopressor for patients with septic shock. When and whether a second agent, such as vasopressin, should ...
Bone fractures are a leading cause of morbidity and disability worldwide, imposing significant clinical and economic burdens on healthcare systems. ...
Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end point...
By helping the neurosurgeon create treatment strategies that increase the survival rate, automotive diagnosis and CT (Computed Tomography) hemorrhage ...
BACKGROUND: This study focused on two Artificial Intelligence chatbots, ChatGPT 3.5 and Google Gemini, as the primary tools for answering questions re...
Automating Electronic Health Records (EHR) documentation can significantly reduce the burden on care providers, particularly in emergency care setting...
BACKGROUND: The aim of this study was to compare the performance of artificial intelligence (AI) in detecting distal radius fractures (DRFs) on plain ...
Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML...
The PERMANENS European project addresses the global public health challenge of self-harm and suicide by developing a machine learning-based Clinical D...
Post-traumatic stress disorder (PTSD) is a complex and prevalent neuropsychiatric condition that arises in response to exposure to a traumatic event. ...
Trauma care coordination in the pediatric intensive care unit (PICU), including personalization of resources based on social determinants of health (S...
In the fast-paced emergency departments, where crises unfold unpredictably, the systematic prioritization of critical patients based on a severity cla...
BACKGROUND: The rapid advancement of Artificial Intelligence (AI) has led to its widespread application across various domains, showing encouraging ou...
Background: There are many challenges and opportunities in the clinical deployment of AI tools in radiology. The current study describes a radiology...
Introduction: Timely identification of intracranial hemorrhage (ICH) subtypes on non-contrast computed tomography is critical for prognosis predicti...
BACKGROUND: Bone fracture risk assessment for osteoporotic patients is essential for implementing early countermeasures and preventing discomfort and ...
Artificial intelligence (AI) has the potential to enhance health care by optimizing patient management and supporting workforce demands. AI-driven cha...