Utilising Artificial Intelligence in Prehospital Emergency Care Systems in Low-and Middle-Income Countries: A Scoping Review

Odhran Mallon^1,2,3*Freddy Lippert^2,4Willem Stassen⁵Marcus Eng Hock Ong⁶Caitlin Dolkart^7,8Thomas Krafft¹Eva Pilot^1*

• ¹Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
• ²Falck, Copenhagen, Denmark
• ³Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, North East England, United Kingdom
• ⁴Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Capital Region of Denmark, Denmark
• ⁵Division of Emergency Medicine, University of Cape Town, Cape Town, South Africa
• ⁶Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
• ⁷Flare Emergency Services, Nairobi, Kenya
• ⁸Rescue.co, Nairobi, Kenya

Introduction: Improvements in prehospital emergency care have the potential to transform patient outcomes globally, but particularly within low-and middle-income countries. While artificial intelligence is being implemented in many healthcare settings, little is known about its use in prehospital emergency care systems. This scoping review aims to uncover how artificial intelligence is currently being used within the prehospital emergency medical services of low-and middle-income countries and assess the implications for future development. Methods: A review of peer-reviewed articles using any artificial intelligence models in prehospital emergency care in low-and middle-income countries was carried out. Medline, Global Health, Embase, CINAHL and Web of Science were searched for studies published between January 2014 and July 2024. Data were extracted, collated and presented in table format and as a narrative synthesis. This scoping review is reported using the PRISMA-ScR guidelines. Results: Sixteen articles were included in the study. Most studies were conducted in China and deep learning models were used in half of the studies. Articles assessing dispatch forecasting were the most common, although artificial intelligence tools are also utilised in classification and disease prediction. There was significant variation in sample sizes throughout the selected studies. Overall, machine learning algorithms outperformed other comparator methods when they were used in all but two studies. Discussion: Limitations included only analysing articles published in English. Additionally, studies that did not identify the model as an artificial intelligence tool, or did not explicitly mention a LMIC in the title or abstract may have been inadvertently excluded. While artificial intelligence can significantly benefit patient care in out-of-hospital settings, the continued development of this technology requires proper consideration for the local sociocultural contexts and challenges in these countries, along with using complete, population-specific datasets. Further research is needed to support advancements in this field and promote the realisation of universal health coverage.