Artificial Intelligence Applications in Prehospital Emergency Care, Epidemiology, Health Information Management, Paramedic Practice, and Health Assistant Services - Mohammed Salami, Ahmed Abdullah; Al-Anazi, Khaled Hamad Salem; Al-Anazi, Mohammed Mahal Salem; Al-Otaibi, Fahad Naif Mahmas; Haqawi, Ahmed Yahya Jaber; Al-Otaibi, Khaled Nashi Bajad; Al-Anzi, Sami Mohammed Naqi; Aljabri, Adel Mohammed; Alharbi, Turki Awad; Al-Anazi, Naif Khalaf; Al Shammari, Abdullah Mohammed; Masmali, Abdulaziz Ismail; et al. (International Journal of Aquatic Research and Environmental Studies, 2025)
Background: Artificial intelligence (AI) has emerged as a transformative technology in prehospital Emergency Medical Services (EMS), addressing growing demands associated with increasing emergency call volumes, aging populations, mass casualty incidents, and healthcare resource limitations. Advanced AI technologies, including machine learning, deep learning, natural language processing, and large language models, have demonstrated considerable potential to improve emergency response efficiency, clinical decision-making, and patient outcomes. Aim: This review aims to examine the applications of artificial intelligence in prehospital emergency care, highlighting its role in emergency call triage, clinical decision support, ambulance dispatch, predictive analytics, resource optimization, and multidisciplinary healthcare practice. Methods: A narrative review of recent peer-reviewed literature was conducted to evaluate current AI methodologies and their applications in EMS. The review synthesizes evidence regarding machine learning algorithms, deep learning models, reinforcement learning, natural language processing, and large language models, while examining their integration into emergency medical workflows and the roles of health assistants, informatics professionals, emergency personnel, and paramedics. Results: Artificial intelligence consistently demonstrated improvements in emergency call classification, patient risk prediction, diagnostic accuracy, ambulance deployment, and operational efficiency. Predictive algorithms enhanced identification of high-risk patients and optimized resource allocation through real-time data analysis. AI-assisted decision-support systems strengthened emergency triage, while natural language processing and large language models improved clinical documentation and communication. Successful implementation depended on high-quality healthcare data, multidisciplinary collaboration, cybersecurity, ethical governance, regulatory compliance, and continuous professional oversight. Conclusion: Artificial intelligence has substantial potential to transform prehospital emergency care by supporting timely clinical decision-making, improving operational performance, and enhancing patient safety. Continued research, prospective validation, and responsible implementation are essential to ensure safe, ethical, and effective integration into emergency medical practice.