Exploring the Applications of Artificial Intelligence in Enhancing Pre-Hospital Care: A Scoping Review
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Date
2024
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Publisher
Queen’s University, Belfast
Abstract
Artificial Intelligence (AI) has the potential to significantly improve pre-hospital care,
especially in emergency medical services (EMS). However, its current application
remains scattered, with varying integration levels across care stages. This scoping review
aims to map and assess existing research on AI applications within pre-hospital care
without focusing on specific AI technologies, such as machine learning (ML), deep
learning (DL), or decision support systems (DSS). The review reflects the current research
landscape, capturing how AI is utilised across critical stages such as call-taking,
dispatch, and on-scene assessment.
Using the framework developed by Arksey and O’Malley (2005), a systematic search was
conducted across multiple databases to identify studies relevant to AI in pre-hospital
care. The scope was deliberately broad to capture a comprehensive view of the available
literature, focusing on identifying areas where further research is needed.
The findings indicate that DSS is commonly used to support decision-making in call-taking
and dispatch, while more advanced AI applications like ML and DL show potential
in predictive analytics and real-time decision-making. However, these technologies are
still in their early stages of real-world implementation.
This review highlights the gaps in AI research, particularly in the later stages of prehospital
care, such as transport and handover. Further exploration is necessary to unlock
AI’s full potential in enhancing EMS operations and outcomes.
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Keywords
EMA, Paramedic, pre hospital care, AI, Artificial intelligence, Ambulance service, Deep learning, Intelligent algorithm, Machine learning
Citation
Harvard