Latest AI and machine learning research in emergency medicine for healthcare professionals.
The rapid identification of medical emergencies through digital communication channels remains a critical challenge in modern healthcare delivery, particularly with the increasing prevalence of telemedicine. This paper presents a novel approach leveraging large language models (LLMs) and prompt engineering techniques for automated emergency detection in medical communications. We developed and e...
One of the most urgent problems is the overcrowding in emergency departments (EDs), caused by an aging population and rising healthcare costs. Patient dispositions have become more complex as a result of the strain on hospital infrastructure and the scarcity of medical resources. Individuals with more dangerous health issues should be prioritized in the emergency room. Thus, our research aims to...
Intracranial hemorrhage (ICH) refers to the leakage or accumulation of blood within the skull, which occurs due to the rupture of blood vessels in o...
The exploration of automated wrist fracture recognition has gained considerable research attention in recent years. In practical medical scenarios, ...
Fractures, particularly in the distal forearm, are among the most common injuries in children and adolescents, with approximately 800 000 cases trea...
Pediatric Emergency Department (PED) overcrowding presents a significant global challenge, prompting the need for efficient solutions. This paper in...
Recent studies show that diffusion models (DMs) are vulnerable to backdoor attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a g...
A seminal paper published by Ledley and Lusted in 1959 introduced complex clinical diagnostic reasoning cases as the gold standard for the evaluatio...
Knee osteoporosis weakens the bone tissue in the knee joint, increasing fracture risk. Early detection through X-ray images enables timely intervent...
Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks. This study aims to develop an...
Given the massive volume of potentially false claims circulating online, claim prioritization is essential in allocating limited human resources ava...
Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to...
No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a c...
Emergency communication systems face disruptions due to packet loss, bandwidth constraints, poor signal quality, delays, and jitter in VoIP systems,...
The success of Emergency Response (ER) scenarios, such as search and rescue, is often dependent upon the prompt location of a lost or injured person...
Emergency response times are critical in densely populated urban environments like New York City (NYC), where traffic congestion significantly imped...
The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing A...
With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements...
Loneliness, or the lack of fulfilling relationships, significantly impacts a person's mental and physical well-being and is prevalent worldwide. Pre...
The field of traumatic hemostasis is currently confronted with numerous challenges, particularly in addressing the treatment of non-compressible torso...