Latest AI and machine learning research in emergency medicine for healthcare professionals.
The use of generative large language models (LLMs) with electronic health record (EHR) data is rapidly expanding to support clinical and research tasks. This systematic review synthesizes current strategies, challenges, and future directions for adapting and evaluating generative LLMs in EHR analyses and applications. We followed the PRISMA guidelines to conduct a systematic review of articles fro...
Missing data is a frequent limitation of database research. Application of machine learning-based models is a promising approach to overcome this limitation. This study aims to evaluate methods for imputing three critical but sometimes missing predictors of important clinical outcomes (e.g. mortality) in hospitalized patients presenting with acute myocardial infarction (AMI): cardiac arrest prior ...
Artificial Intelligence (AI) is rapidly changing the legal landscape of radiology. Results from a previous experiment suggested that providing AI erro...
Osteoporosis is characterized by diminished BMD and deteriorated bone microstructure, significantly increasing fracture susceptibility. This study lev...
Trauma remains a leading cause of morbidity and mortality in part due to secondary organ injury and infection. Yet, our ability to predict the downstr...
To evaluate and compare the real-time Sepsis risk Artificial intelligence algorithm For Emergency department WAITing room (SAFE-WAIT) model with the s...
Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers globally. The current focus on AI deployments in urban area...
Intracranial hemorrhage (ICH), whether spontaneous or traumatic, is a neurological emergency with high morbidity and mortality. Accurate assessment of...
Artificial intelligence (AI) is increasingly used to support clinical decision-making, particularly in primary care triage. However, few studies have ...
Despite policy support, inappropriate use of emergency contraception (EC) in Ethiopia contributes to high rates of unintended pregnancy and maternal m...
This study explores clinician understanding and perception at site lead level towards machine learning (ML) decision support tools for paediatric rela...
Opioid use disorder (OUD) is a chronic condition in which an individual engages in the persistent use of opioids that causes significant distress and ...
To determine the impact of the temperature parameter on GPT-4o’s diagnostic accuracy when evaluating emergency medicine cases and assess the effect on...
The ongoing war in Ukraine has exposed young adults to sustained psychological stress, elevating their risk of developing post-traumatic stress disord...
There is great potential for artificial Intelligence (AI) and machine learning (ML) to support decision making in emergency departments (ED), however ...
Stroke care generates vast free-text records that slow chart review and hamper data reuse. Large language models (LLMs) have been trialed as a remedy ...
Preterm birth, defined as birth occurring before 37 weeks of gestation, poses a significant and enduring public health challenge, with substantial emo...
Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few r...
Adequate self-harm surveillance is a key part of suicide prevention efforts. Our prior work has demonstrated the efficacy of an artificial intelligenc...
Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly follow...