AIMC Topic: Emergency Medical Services
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Emergency medical services providers' perspectives on the use of artificial intelligence in prehospital identification of stroke- a qualitative study in Norway and Sweden.
BMC emergency medicine
Jul 28, 2025
BACKGROUND: Stroke is a large and increasing health challenge, leading to acquired physical disability and mortality. A rapid diagnostic assessment in the acute phase of a stroke is crucial and highly time dependent. Studies suggest that artificial i...
Evolving role of point-of-care ultrasound in prehospital emergency care: a narrative review.
Scandinavian journal of trauma, resuscitation and emergency medicine
Jul 14, 2025
Point-of-care ultrasound is an emerging technology in prehospital emergency care, covering a wide range of medical and traumatic disease patterns. As an ad-hoc imaging modality, it is performed on-scene and during ground or aeromedical transport, ena...
Machine learning to improve predictive performance of prehospital early warning scores.
Scientific reports
Jul 1, 2025
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...
Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.
BMJ health & care informatics
Jun 6, 2025
OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the EC...
Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.
BMC medical informatics and decision making
Jun 3, 2025
BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently un...
A Machine Learning Trauma Triage Model for Critical Care Transport.
JAMA network open
Jun 2, 2025
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...
Enhancing clinical decision support with physiological waveforms - A multimodal benchmark in emergency care.
Computers in biology and medicine
Apr 30, 2025
BACKGROUND: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including r...
Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).
Scientific reports
Apr 24, 2025
The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimately saving lives. Delays in ambulance arrival can have serious consequences for patient health and s...
Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.
International journal of injury control and safety promotion
Apr 11, 2025
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...
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