Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review
IntroductionImprovements in prehospital emergency care have the potential to transform patient outcomes globally, but particularly within low-and middle-income countries. Whilst artificial intelligence is being implemented in many healthcare settings, little is known about its use in prehospital eme...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1604231/full |
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| author | Odhran Mallon Odhran Mallon Odhran Mallon Freddy Lippert Freddy Lippert Willem Stassen Marcus Eng Hock Ong Caitlin Dolkart Caitlin Dolkart Thomas Krafft Eva Pilot |
| author_facet | Odhran Mallon Odhran Mallon Odhran Mallon Freddy Lippert Freddy Lippert Willem Stassen Marcus Eng Hock Ong Caitlin Dolkart Caitlin Dolkart Thomas Krafft Eva Pilot |
| author_sort | Odhran Mallon |
| collection | DOAJ |
| description | IntroductionImprovements in prehospital emergency care have the potential to transform patient outcomes globally, but particularly within low-and middle-income countries. Whilst 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.MethodsA 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.ResultsSixteen 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.DiscussionLimitations 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. Whilst 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.Systematic review registrationhttps://doi.org/10.17605/OSF.IO/9VS2M, osf.io/9vs2m. |
| format | Article |
| id | doaj-art-0c492a2cedc64e97ba8ade756e21b587 |
| institution | DOAJ |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-0c492a2cedc64e97ba8ade756e21b5872025-08-20T03:21:43ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-06-011310.3389/fpubh.2025.16042311604231Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping reviewOdhran Mallon0Odhran Mallon1Odhran Mallon2Freddy Lippert3Freddy Lippert4Willem Stassen5Marcus Eng Hock Ong6Caitlin Dolkart7Caitlin Dolkart8Thomas Krafft9Eva Pilot10Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, NetherlandsFalck, Copenhagen, DenmarkFaculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United KingdomFalck, Copenhagen, DenmarkFaculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DenmarkDivision of Emergency Medicine, University of Cape Town, Cape Town, South AfricaDepartment of Emergency Medicine, Singapore General Hospital, Singapore, SingaporeRescue.co, Nairobi, KenyaFlare Emergency Services, Nairobi, KenyaFaculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, NetherlandsFaculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, NetherlandsIntroductionImprovements in prehospital emergency care have the potential to transform patient outcomes globally, but particularly within low-and middle-income countries. Whilst 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.MethodsA 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.ResultsSixteen 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.DiscussionLimitations 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. Whilst 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.Systematic review registrationhttps://doi.org/10.17605/OSF.IO/9VS2M, osf.io/9vs2m.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1604231/fullartificial intelligencemachine learningprehospitalemergency medical servicesLMICemergency patient care |
| spellingShingle | Odhran Mallon Odhran Mallon Odhran Mallon Freddy Lippert Freddy Lippert Willem Stassen Marcus Eng Hock Ong Caitlin Dolkart Caitlin Dolkart Thomas Krafft Eva Pilot Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review Frontiers in Public Health artificial intelligence machine learning prehospital emergency medical services LMIC emergency patient care |
| title | Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review |
| title_full | Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review |
| title_fullStr | Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review |
| title_full_unstemmed | Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review |
| title_short | Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review |
| title_sort | utilising artificial intelligence in prehospital emergency care systems in low and middle income countries a scoping review |
| topic | artificial intelligence machine learning prehospital emergency medical services LMIC emergency patient care |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1604231/full |
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