Latest AI and machine learning research in emergency medicine for healthcare professionals.
BACKGROUND: Emergency department (ED) visits have risen in the United States, with demand for emergency care exceeding supply. Resultant ED crowding harms patients, causes staff burnout, and places financial strain on hospitals, payers, and patients alike. Digital tools, including those leveraging artificial intelligence (AI), offer promise for driving efficiency and mitigating the harms of crowdi...
The rapid ageing of the global population is reshaping oral health care delivery, with increasing numbers of older adults receiving care in home-based or dependent settings where access to dental services remains limited. Although teledentistry has emerged as a potential solution, its current implementation is constrained by passive communication models, variable diagnostic reliability, and limite...
BACKGROUND: Post-traumatic stress disorder (PTSD) is a stressor-related disorder that affects a significant proportion of the population worldwide. De...
BACKGROUND: Conventional clinical scoring systems and contrast-enhanced computed tomography (CECT) interpretation provide limited accuracy in predicti...
Identity is a multi-level integrative process involving coordinated development across affective, symbolic, and embodied systems-a process of progress...
BACKGROUND: Artificial intelligence (AI) detection tools for intracranial hemorrhage (ICH) are increasingly integrated into radiology workflows. In re...
Per- and polyfluoroalkyl substances (PFASs) pose persistent challenges to urban environmental management due to their complex sources, ongoing substit...
BACKGROUND: Severe trauma remains a leading cause of admission to the intensive care unit. The Trauma and Injury Severity Score (TRISS) is an establis...
In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites, using t...
Artificial intelligence (AI) has shown promise in detecting and characterizing musculoskeletal diseases from radiographs. However, most existing model...
Financial fraud detection requires screening massive transaction networks where evolving topologies, extreme label sparsity, and asymmetric misclassif...
BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) like nonylphenol (NP) and octylphenol (OP) are widespread water pollutants. Their estrogen-...
Rapid prediction of urban pluvial flooding is an important tool for mitigating current urban flooding disasters. This paper constructs a fast predicti...
BACKGROUND: Large language models (LLMs) have shown promising results in medical decision support; Background: Large language models (LLMs) have demon...
PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...
PURPOSE: To validate the performance of an AI system (TRIAGE) for cancer trial eligibility screening using real-world longitudinal electronic health r...
Sodium p-perfluorous nonenoxybenzene sulfonate (OBS), widely used as an alternative to per- and polyfluoroalkyl substances, has been implicated in tox...
BACKGROUND: Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, su...
BACKGROUND: Incomplete prehospital documentation remains a major challenge, compromising continuity of care and data quality. Manual documentation is ...
BACKGROUND: Carfentanil is an extremely potent synthetic fentanyl analogue often present at trace levels alongside other fentanyl analogues and long-a...